commit 1e7cc2a71b24cd72a8864ea91775dc1c84f478d7 Author: Jinotech Date: Sun Sep 20 05:17:11 2026 +1200 chore(repo): initialize reproducible research workspace diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ccc6913 --- /dev/null +++ b/.gitignore @@ -0,0 +1,45 @@ +# Raw and local reference data +/Dataset/ +/LLM-based Agent构建/ + +# Project-local runtimes and package libraries +/.python/ +/.venv-research/ +/.r-library/ +/.tools/ + +# Downloads and caches +/.downloads/ +/.cache/ +/.uv-cache/ +/Model.cachepytensor/ +**/__pycache__/ +**/node_modules/ +*.py[cod] +.ipynb_checkpoints/ + +# Rebuildable native outputs +/research/environment/cuda-smoke.exe +*.obj +*.exp +*.lib +*.pdb + +# R and editor state +.Rhistory +.RData +.Ruserdata +.Rproj.user/ +.vscode/ +.idea/ + +# OS metadata +Thumbs.db +.DS_Store + +# Model weights and large binary checkpoints +*.safetensors +*.ckpt +*.pth +*.pt +*.onnx diff --git a/.learnings/ERRORS.md b/.learnings/ERRORS.md new file mode 100644 index 0000000..b85a394 --- /dev/null +++ b/.learnings/ERRORS.md @@ -0,0 +1,671 @@ +# Errors + +Command failures and integration errors. + +--- + +## [ERR-20260920-006] powershell-empty-pipe + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +A final artifact verification command piped directly from a PowerShell `foreach` statement and produced an empty-pipe parser error. + +### Error +`ParserError: An empty pipe element is not allowed.` + +### Context +Read-only existence and size verification for the four Stage 0 deliverables and Progression.md. + +### Suggested Fix +Collect `foreach` output into an array first, then pipe the array to `Format-Table`. + +### Metadata +- Reproducible: yes +- Related Files: `Progression.md` +- Pattern-Key: shell.nonzero-exit + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: The corrected command verified all required deliverables exist and contain data. + +--- + +## [ERR-20260920-004] progression-patch-context + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: docs + +### Summary +A combined large patch failed because one Progression.md context line did not match byte-for-byte. + +### Error +`apply_patch verification failed: Failed to find expected lines` + +### Context +Attempted to create the project charter and update multiple distant sections of the progress archive in one patch. + +### Suggested Fix +Split artifact creation from progress updates and use smaller, exact context anchors confirmed with `rg`. + +### Metadata +- Reproducible: unknown +- Related Files: `Progression.md`, `research/protocol/project_charter.md` +- Pattern-Key: config.patch-context-mismatch + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Re-read the mandatory context and split the operation into smaller patches. +- **Recurrence-Count**: 2 +- **Last-Seen**: 2026-09-20 + +--- + +## [ERR-20260920-005] spreadsheet-marker-path + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The spreadsheet skill's operation marker was not located at the dependency-root path shown by the generic example. + +### Error +`Cannot find module ...dependencies\\container_tools\\mark_artifact_operation_started.mjs` + +### Context +Preparing to author the literature matrix with the bundled artifact runtime. + +### Suggested Fix +Resolve the marker from the installed spreadsheet skill directory rather than the dependency root. + +### Metadata +- Reproducible: yes +- Related Files: `research/protocol/literature_matrix.xlsx` +- Pattern-Key: config.missing-script + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Located the marker under the versioned spreadsheet skill and ran it successfully before authoring. + +--- + +## [ERR-20260920-002] restricted-system-inventory + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +WMI, `Get-Volume`, and `systeminfo` returned access denied during the environment audit. + +### Error +`Access denied` / `拒绝访问` + +### Context +Attempted read-only OS, RAM, and volume inventory inside the managed Windows execution environment. + +### Suggested Fix +Use unprivileged fallbacks: .NET runtime information, `Microsoft.VisualBasic.Devices.ComputerInfo`, and `Get-PSDrive`. + +### Metadata +- Reproducible: yes +- Related Files: `research/audit/environment_audit.md` +- Pattern-Key: fs.permission-denied + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Fallback interfaces returned OS architecture, physical RAM, and filesystem free space without escalation. + +--- + +## [ERR-20260920-003] python-package-detection + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The first package audit used import names derived mechanically from distribution names and produced unreliable missing-package results; a follow-up also attempted to import absent `psutil`. + +### Error +`ModuleNotFoundError: No module named 'psutil'` + +### Context +Auditing the local Python scientific stack. Distribution names such as `scikit-learn` do not map mechanically to import names. + +### Suggested Fix +Query installed distribution versions directly with `importlib.metadata.version()` and handle `PackageNotFoundError` without importing packages. + +### Metadata +- Reproducible: yes +- Related Files: `research/audit/environment_audit.md` +- Pattern-Key: deps.module-not-found + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: The final package table was generated directly from distribution metadata; hardware memory used a .NET fallback. + +--- + +## [ERR-20260920-001] pdf-to-markdown-encoding + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: docs + +### Summary +PDF text extraction failed because Python wrote a Unicode character that could not be encoded using the Windows default code page. + +### Error +`UnicodeEncodeError: 'gbk' codec can't encode character` + +### Context +Attempted to extract text from `Document/项目的正式定位.pdf` before passing it through Pandoc. + +### Suggested Fix +Force UTF-8 output with `PYTHONIOENCODING=utf-8`. + +### Metadata +- Reproducible: yes +- Pattern-Key: shell.encoding-error + +--- +## [ERR-20260920-007] winget-access-denied + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: pending +**Area**: infra + +### Summary +The Windows Package Manager launcher exists but cannot be executed from the managed environment. + +### Error +`Program 'winget.exe' failed to run: 系统无法访问此文件。` + +### Context +Attempted to inventory/install R, Python, and CUDA Toolkit through the standard Windows package manager after the user authorized environment remediation. + +### Suggested Fix +Use vendor-provided official installers, or retry `winget` outside the restricted environment if explicit escalation makes the launcher accessible. + +### Metadata +- Reproducible: yes +- Related Files: `Progression.md`, `research/audit/environment_audit.md` +- Pattern-Key: tool.access-denied + +--- +## [ERR-20260920-008] uv-python-patch-path + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The virtual-environment command hard-coded a Python 3.12 patch-version path that differed from the version resolved by `uv`. + +### Error +`No interpreter found at path .python\\cpython-3.12.11-windows-x86_64-none\\python.exe` + +### Context +`uv python install 3.12` correctly resolved CPython 3.12.14, while the following command assumed 3.12.11. + +### Suggested Fix +Resolve the installed interpreter path from `uv python find` or enumerate the installation directory rather than hard-coding a patch version. + +### Metadata +- Reproducible: yes +- Related Files: `Progression.md` +- Pattern-Key: config.version-path-mismatch + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Located `E:\\Model\\.python\\cpython-3.12.14-windows-x86_64-none\\python.exe`; subsequent environment creation will use this exact path. + +--- +## [ERR-20260920-009] research-env-smoke-failures + +**Logged**: 2026-09-20 +**Priority**: high +**Status**: pending +**Area**: infra + +### Summary +The first research-environment runtime test found cache-permission, package-API, and CPU-only PyTorch problems despite a clean dependency-metadata check. + +### Error +`PermissionError` for PyTensor/Matplotlib/skrub cache paths; `TypeError: check_array() got an unexpected keyword argument 'force_all_finite'`; `torch.cuda.is_available() is false`. + +### Context +Executed PyMC sampling, semopy CFA, factor analysis, module imports, and a CUDA matrix multiplication in the new Python 3.12 environment. + +### Suggested Fix +Redirect all caches into the writable project tree, pin a factor-analyzer-compatible scikit-learn release, and reinstall a matched Torch/TorchVision/TorchAudio set from the official CUDA wheel index. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/smoke_test.py`, `research/environment/smoke-test-result.json`, `Progression.md` +- Pattern-Key: deps.runtime-compatibility + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Redirected caches into `E:\\Model\\.cache`, pinned scikit-learn 1.6.1, and installed matched CUDA 13.0 builds of Torch 2.14.0 and TorchVision 0.29.0. Torchaudio was removed because no same-generation official wheel exists and the project has no audio workload. Final closure is supported by the second smoke test recorded in `Progression.md`. + +--- +## [ERR-20260920-010] torchaudio-release-mismatch + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The requested CUDA 13.0 Torch 2.14 stack could not be resolved because the official index does not publish torchaudio 2.14.0 for Windows/Python 3.12. + +### Error +`No solution found ... there is no version of torchaudio==2.14.0`. + +### Context +Attempted to align torch, torchvision, and torchaudio at the Torch 2.14 generation using the official CUDA 13.0 wheel index. + +### Suggested Fix +Do not install torchaudio when the project has no audio workload; keep only matched torch and torchvision CUDA builds. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/python-requirements.in`, `Progression.md` +- Pattern-Key: deps.release-mismatch + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Removed torchaudio from the project requirements and smoke-test contract after verifying the official index. + +--- +## [ERR-20260920-011] r-installer-file-race + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The silent R installer returned before the first immediate `Rscript` invocation observed the completed target files. + +### Error +`Rscript.exe is not recognized` immediately after installer return. + +### Context +Installed R 4.6.1 into the project-local tools directory with the official CRAN Windows installer. + +### Suggested Fix +After installer return, poll for the expected executable or perform a separate verification step before declaring failure. + +### Metadata +- Reproducible: unknown +- Related Files: `Progression.md` +- Pattern-Key: installer.completion-race + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: A follow-up inventory showed a complete installation; `Rscript` reported R 4.6.1 x64 successfully. + +--- +## [ERR-20260920-012] r-smoke-false-positive + +**Logged**: 2026-09-20 +**Priority**: high +**Status**: resolved +**Area**: tests + +### Summary +The first R smoke test marked the mirt test as passed even though the fitted model reported non-convergence. + +### Error +The JSON result contained `status: passed` together with `converged: false`. + +### Context +The test wrapper treated any expression that returned without raising an R error as success; the mirt block returned its optimizer flag but did not assert it. + +### Suggested Fix +Use data generated from an identifiable 2PL model and raise an explicit error unless `fit@OptimInfo$converged` is true. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/r-smoke-test.R`, `research/environment/r-smoke-test-result.json`, `Progression.md` +- Pattern-Key: tests.missing-assertion + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Added an explicit convergence assertion, identifiable 2PL simulation, and a higher EM-cycle ceiling. A second run is required for environmental acceptance. + +--- +## [ERR-20260920-013] tool-poll-key-escaping + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +Two CUDA installer status polls failed because a hyphenated nested-tool argument key was not quoted in JavaScript. + +### Error +`SyntaxError: Unexpected string` / `SyntaxError: Invalid or unexpected token`. + +### Context +Polling an already-running installer session; the failed polls did not send input or alter the installer process. + +### Suggested Fix +Quote hyphenated object keys, e.g. `{"yield-time_ms": 30000}`. + +### Metadata +- Reproducible: yes +- Related Files: `Progression.md` +- Pattern-Key: tooling.javascript-object-key + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Corrected the key quoting and continued polling the same session until the installer returned exit code 0. + +--- +## [ERR-20260920-014] vsdevcmd-nested-quoting + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +The first CUDA compile command failed before invoking the compiler because nested `cmd.exe` quoting truncated the Visual Studio path at the first space. + +### Error +`'""C:\\Program' is not recognized as an internal or external command`. + +### Context +Attempted to chain `VsDevCmd.bat`, nvcc, and the smoke executable in a single quoted `cmd.exe /c` string. + +### Suggested Fix +Capture the environment emitted by `VsDevCmd.bat && set`, import it into the PowerShell process, then invoke nvcc directly with argument boundaries managed by PowerShell. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/cuda-smoke.cu`, `Progression.md` +- Pattern-Key: shell.windows-nested-quoting + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Switched to importing the Visual Studio environment before compiler invocation; the corrected compile is recorded separately in `Progression.md`. + +--- +## [ERR-20260920-015] nvcc-host-compiler-path + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +nvcc could not find `cl.exe` after Visual Studio environment variables were copied from a child cmd process into PowerShell. + +### Error +`nvcc fatal: Cannot find compiler 'cl.exe' in PATH`. + +### Context +The MSVC component and compiler file were installed, but cross-shell environment import was insufficient for nvcc host-tool discovery. + +### Suggested Fix +Run `vcvars64.bat`, nvcc, and the resulting binary inside one cmd process using a checked-in build script. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/cuda-smoke-build.cmd`, `research/environment/cuda-smoke.cu`, `Progression.md` +- Pattern-Key: toolchain.environment-propagation + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Created a single-shell build script after verifying the exact MSVC 14.44 compiler path with vswhere. + +--- +## [ERR-20260920-016] lock-cache-permissions + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +Python and R lock export failed because uv and renv attempted to initialize caches under restricted user-profile directories. + +### Error +`Failed to initialize cache ... uv\\cache` and `failed to create directory ... R/cache/R/renv`. + +### Context +The runtime caches had been redirected, but the package-manager-specific cache variables were not yet included. + +### Suggested Fix +Set `UV_CACHE_DIR` and `RENV_PATHS_ROOT` to writable project-local directories for both activation and export commands. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/activate-research.ps1`, `Progression.md` +- Pattern-Key: fs.package-manager-cache-permission + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Added project-local uv and renv cache roots; lock export is retried as a separate audited action. + +--- +## [ERR-20260920-017] r-lock-export-schema + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +The R lock snapshot was written, but the auxiliary installed-package export failed due to an assumed metadata column and an incomplete library search path. + +### Error +`subscript out of bounds` for the `Repository` column; renv warned that R-recommended packages were missing. + +### Context +The export indexed a fixed column list from `installed.packages(lib.loc=project_library)` and passed only the project library to renv. + +### Suggested Fix +Combine the project library with `.Library`, and select only metadata columns that actually exist before writing CSV. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/renv.lock`, `Progression.md` +- Pattern-Key: data.schema-assumption + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: The corrected export uses both library roots and intersects requested fields with returned column names; final verification is a separate action. + +--- +## [ERR-20260920-018] apply-patch-same-file-multiple-operations + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: docs + +### Summary +A patch attempted to delete and add the same audit file in one invocation and was rejected before making changes. + +### Error +`multiple operations target ... environment_audit.md`. + +### Context +Replacing the completed environment audit while also adding usage documentation and updating progression state. + +### Suggested Fix +Split full-file replacement into separate delete and add operations, then patch other files separately. + +### Metadata +- Reproducible: yes +- Related Files: `research/audit/environment_audit.md`, `Progression.md` +- Pattern-Key: tooling.patch-operation-conflict + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: No file changed in the rejected patch; the replacement is retried in valid discrete operations. + +--- +## [ERR-20260920-019] apply-patch-hunk-order + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: docs + +### Summary +A combined README/progression patch was rejected because two progression hunks were ordered opposite to their line order in the file. + +### Error +`Failed to find expected lines in Progression.md` even though the lines existed. + +### Context +One hunk targeted a later risk-table line before another hunk targeted an earlier confirmed-facts line. + +### Suggested Fix +Order update hunks from top to bottom within each file, or split unrelated file additions and updates. + +### Metadata +- Reproducible: yes +- Related Files: `research/environment/README.md`, `Progression.md` +- Pattern-Key: tooling.patch-hunk-order + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Added the README separately and applied progression hunks in file order. + +--- +## [ERR-20260920-020] git-dubious-ownership + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +Git refused repository status checks after initialization because the workspace owner differs from the managed execution account. + +### Error +`fatal: detected dubious ownership in repository at 'E:/Model'`. + +### Context +The repository directory is owned by the Windows Administrators group while commands run as CodexSandboxOffline. + +### Suggested Fix +Add only the exact repository path to Git's global `safe.directory`; do not use a wildcard. + +### Metadata +- Reproducible: yes +- Related Files: `.git`, `Progression.md` +- Pattern-Key: git.safe-directory + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: The exact-path exception is applied as the next audited action before staging. + +--- +## [ERR-20260920-021] git-candidate-node-modules + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +The first Git candidate set unintentionally included a nested `node_modules` tree, expanding the proposed commit to 7,832 files and about 321 MiB. + +### Error +Candidate-size audit showed large native modules and generated JavaScript under `research/experiments/node_modules`. + +### Context +The initial `.gitignore` covered research runtimes and caches but omitted generic nested Node dependencies. + +### Suggested Fix +Add `**/node_modules/` before staging and rerun the size audit. + +### Metadata +- Reproducible: yes +- Related Files: `.gitignore`, `Progression.md` +- Pattern-Key: git.generated-dependency-tree + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: Added the nested dependency ignore rule before any files were staged. + +--- +## [ERR-20260920-022] git-index-lock-permission + +**Logged**: 2026-09-20 +**Priority**: medium +**Status**: resolved +**Area**: infra + +### Summary +The first staging attempt could read the repository but could not create `.git/index.lock` under the managed filesystem profile. + +### Error +`fatal: Unable to create 'E:/Model/.git/index.lock': Permission denied`. + +### Context +The workspace is writable, while repository metadata is protected and requires the authorized elevated Git operation path. + +### Suggested Fix +Retry only the required Git index/commit operations with escalation; do not change workspace ownership or weaken filesystem permissions. + +### Metadata +- Reproducible: yes +- Related Files: `.git`, `Progression.md` +- Pattern-Key: git.index-lock-permission + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: No files were staged in the failed attempt; the audited candidate set is retried with scoped Git authorization. + +--- +## [ERR-20260920-023] git-null-excludesfile + +**Logged**: 2026-09-20 +**Priority**: low +**Status**: resolved +**Area**: infra + +### Summary +Git for Windows rejected `NUL` as an explicit global excludes file override. + +### Error +`fatal: cannot use NUL as an exclude file`. + +### Context +The override was intended only to silence warnings from an unreadable user-level ignore file; project ignore rules were already valid. + +### Suggested Fix +Remove the override and tolerate the harmless warning, or use an actual readable empty file if silence is necessary. + +### Metadata +- Reproducible: yes +- Related Files: `.gitignore`, `Progression.md` +- Pattern-Key: git.excludesfile-windows-device + +### Resolution +- **Resolved**: 2026-09-20 +- **Notes**: The override is removed; no index changes occurred in the failed attempt. + +--- diff --git a/.learnings/FEATURE_REQUESTS.md b/.learnings/FEATURE_REQUESTS.md new file mode 100644 index 0000000..3be3c58 --- /dev/null +++ b/.learnings/FEATURE_REQUESTS.md @@ -0,0 +1,5 @@ +# Feature Requests + +Capabilities requested by the user. + +--- diff --git a/.learnings/LEARNINGS.md b/.learnings/LEARNINGS.md new file mode 100644 index 0000000..470d5d7 --- /dev/null +++ b/.learnings/LEARNINGS.md @@ -0,0 +1,5 @@ +# Learnings + +Corrections, insights, and knowledge gaps captured during development. + +--- diff --git a/AGENTS.md b/AGENTS.md new file mode 100644 index 0000000..8f209b9 --- /dev/null +++ b/AGENTS.md @@ -0,0 +1,33 @@ +# 项目工作约定 + +## 强制进度协议 + +本项目的研究工作以以下两个文件为持续上下文： + +- `Document/research-workflow.md`：研究阶段、Gate、交付物与停机条件的唯一工作流基准。 +- `Progression.md`：项目当前状态、行动记录、证据、未解决问题与下一步的唯一进度档案。 + +每次执行任何项目研究行动时，必须遵守： + +1. 行动前完整读取 `Document/research-workflow.md` 和 `Progression.md`。 +2. 对照工作流确认当前阶段、允许的动作、预期交付物和适用 Gate。 +3. 执行动作，不把计划、既有报告或未经复核的材料记作新结果。 +4. 行动后立即编辑 `Progression.md`，至少记录时间、阶段、动作、输入、输出、检查结果、未解决问题和下一步。 +5. 只有交付物及检查证据满足工作流要求时，才可把阶段标为 `passed`；证据不足时使用 `in_progress`、`conditional`、`blocked` 或 `failed`。 + +该协议适用于数据盘点、文献检索、代码或模型开发、统计分析、文档写作、审查和文件管理。只读的临时诊断若直接服务于同一个已登记行动，可合并为一条行动记录，但仍须在行动结束时更新 `Progression.md`。 + +## 强制 Git 协议 + +本项目使用根目录 Git 仓库保存可复现的研究历史。每次“推进一步”必须形成一个边界清楚、可审计的提交，并遵守以下顺序： + +1. 行动开始前，在读取工作流与进度档案的同时检查 `git status --short`，识别并保护用户已有或无关改动。 +2. 只修改当前行动范围内的文件；不得擅自覆盖、撤销或混入用户的其他改动。 +3. 完成行动和验证后，先更新 `Progression.md`，写明输出、检查结果、提交/推送状态与下一步。 +4. 检查 `git diff` 和待提交文件清单，只暂存本行动相关文件；严禁提交凭据、原始/受限数据、解释器、虚拟环境、R library、安装器、缓存、模型权重或可重建的大型二进制。 +5. 为每个完成的推进动作创建一个非交互式 Git 提交；提交信息应简洁说明阶段与动作，不得把多个无关行动压成一个提交。 +6. 若已配置远程与上游分支，提交成功后必须立即执行 `git push`，并把结果记录到 `Progression.md`。 +7. 若没有远程、缺少凭据或推送失败，不得声称已推送；在 `Progression.md` 标为 `push_pending` 或 `push_failed` 并记录原因。远程一旦配置，开始下一项研究行动前应先推送所有待推提交。 +8. 删除、改写历史、强制推送、变基已共享提交或变更远程地址仍需用户明确授权；本协议不自动授权破坏性 Git 操作。 + +根目录 `.gitignore` 是版本化边界的一部分。新增大文件类型、数据目录、模型缓存或生成物时，必须先判断是否应忽略；不得依赖事后从历史中清除敏感或超大文件。 diff --git a/Document/language-conditioned-psychometric-harmonization-model.md b/Document/language-conditioned-psychometric-harmonization-model.md new file mode 100644 index 0000000..97ea87b --- /dev/null +++ b/Document/language-conditioned-psychometric-harmonization-model.md @@ -0,0 +1,151 @@ +# 跨国家、跨年份与跨调查工具的语言条件化心理测量协调模型 + +## 一、项目定位 + +本项目拟开发一个可迁移的 **language-conditioned psychometric harmonization model**，用于协调不同国家、不同年份和不同调查工具中的青少年心理健康与自杀相关表型。 + +模型的核心问题不是比较 XGBoost、随机森林和深度学习的预测性能，也不是主要预测某个人是否会自杀，而是判断不同问卷措辞、语言、时间窗口、答案编码和文化背景是否测量了同一个心理构念，并将这些不完全等价的测量结果映射到统一、可比较的潜在表型空间。 + +## 二、研究目标 + +模型需要同时学习： + +1. 问卷题目在语义上测量的心理构念； +2. 题目在真实人群中的心理测量属性； +3. 国家、年份、调查工具和人群背景对题目测量结果的影响； +4. 不同问卷之间可比较的个体潜在表型及其不确定性。 + +最终目标是让不同国家、不同年份、不同调查工具和不同问法测得的心理健康/自杀相关表型进入同一个 measurement space，并能够迁移到训练时未见过的新题目、新问卷版本和新国家。 + +## 三、模型输入 + +### 1. 题目语义与测量信息 + +- question text； +- construct； +- time window； +- response options； +- response encoding； +- language； +- 可选的题目来源、题目版本和翻译信息。 + +### 2. 调查背景信息 + +- country； +- year； +- survey/instrument； +- questionnaire version； +- population； +- 其他与抽样、文化和调查实施有关的 context。 + +### 3. 受访者回答数据 + +- person × item response matrix； +- survey weight； +- PSU； +- stratum； +- 可选的年龄、性别等协变量，以及缺失模式和调查实施信息。 + +## 四、模型核心 + +模型采用 **semantic encoder + hierarchical psychometric model** 的架构。语义编码器从题目文本、时间窗口、答案选项和语言等信息中提取题目表示；分层心理测量模型结合 survey context 与 response patterns，学习题目在不同调查环境下的测量属性，并估计个体潜在表型。 + +核心不是简单地使用 LLM embedding 进行分类，而是学习一个从 + +> item semantics + survey context + response patterns + +到 psychometric properties 的可迁移映射。 + +### 主要估计对象 + +- **Discrimination（a）**：题目区分不同潜在水平受访者的能力； +- **Threshold / difficulty（b）**：有序或分类回答对应的潜在水平阈值； +- **DIF（Differential Item Functioning）**：题目在不同国家、年份、语言、调查工具或人群中的测量差异； +- **Latent phenotype（θ）**：经过跨问卷校准后的个体潜在心理表型； +- **Uncertainty**：题目参数、DIF、个体得分和群体估计的不确定性。 + +可根据题目类型使用适当的 IRT、graded response、multidimensional 或其他层级心理测量形式；具体形式应服从题目的 construct、答案编码和数据结构，而不预先限定为单一模型。 + +## 五、模型输出 + +### 1. Item-level output + +- 题目所属 construct； +- discrimination 参数 (a)； +- threshold/difficulty 参数 (b)； +- 国家、年份、语言、调查工具和人群层面的 DIF； +- 与已有题目的等值关系、语义相似度和可替代性； +- 参数估计及其 uncertainty。 + +### 2. Person-level output + +- 经过跨问卷校准的 latent phenotype score (\theta)； +- 个体得分的 uncertainty； +- 必要时输出多维 latent phenotype，而不是强制压缩为单一分数。 + +### 3. Population-level output + +- 不同国家和年份的 harmonized phenotype distribution； +- 经过调查设计校正的 prevalence 或分位数估计； +- 跨调查、跨国家、跨年份的可比群体差异； +- 与 survey weight、PSU、stratum 相一致的标准误和不确定性区间。 + +## 六、最重要的迁移能力 + +模型的关键创新不是证明“LLM 让 AUC 提高了”，而是证明模型学习到了一个 **transferable psychometric measurement function**。 + +训练时应有意识地完全留出部分： + +- item/question； +- questionnaire 或 instrument version； +- country； +- survey year； +- population subgroup。 + +在测试阶段，只向模型提供新题目的语义、调查 context 和少量 response data，评估其能否： + +1. zero-shot 或 few-shot 恢复新题目的 psychometric parameters； +2. 识别新题目与已有题目的 construct 和等值关系； +3. 估计新国家、新问卷或新版本中的 DIF； +4. 将新调查回答映射回已有的统一 latent space； +5. 给出可靠的不确定性，而不是只输出一个过度确定的点估计。 + +因此，**unseen item / unseen survey / unseen country transfer** 应作为模型最重要的能力和主要验证目标。 + +## 七、验证框架 + +验证重点应从普通预测性能转向测量等价性、校准能力和迁移能力，包括： + +- 留出题目后的 item parameter recovery； +- 留出问卷版本后的 harmonization； +- 留出国家或文化背景后的 transfer； +- 少量回答条件下的 few-shot calibration； +- 参数、个体分数和群体分布的不确定性校准； +- 与 anchor items、已建立量表或专家标注的一致性； +- 对问卷措辞、时间窗口和答案编码变化的敏感性分析； +- 使用 survey weight、PSU、stratum 后的群体估计有效性。 + +评价指标可包括参数恢复误差、DIF 检测准确性、latent score 一致性、群体分布距离、测量不变性指标、区间覆盖率和跨域校准误差。AUC、F1 等分类指标最多作为辅助分析，不应作为项目的主要成功标准。 + +## 八、构念可扩展性 + +架构应保持 construct-agnostic。自杀相关表型可以作为主要 benchmark，但模型不应被设计成只能处理 suicide risk。后续可扩展到： + +- depression； +- psychological distress； +- anxiety； +- substance use； +- wellbeing； +- 其他可通过问卷题目测量的心理健康构念。 + +如果同一套语义条件化与心理测量协调机制能够跨 construct 泛化，将进一步支持该方法作为通用的跨文化、跨调查心理测量基础设施，而非单一任务的预测模型。 + +## 九、最简洁的项目概括 + +**Input：** item semantics + survey context + response data + +**Model：** semantic encoder + hierarchical psychometric model + +**Output：** item parameters + DIF + harmonized latent phenotype + uncertainty + +**最终目的：** 将不同国家、不同年份、不同调查工具和不同问法测得的心理健康/自杀相关表型映射到统一、可比较且可迁移的 measurement space，并对训练中未见过的新题目、新问卷和新国家完成 zero-shot 或 few-shot psychometric calibration 与 harmonization。 diff --git a/Document/research-workflow.md b/Document/research-workflow.md new file mode 100644 index 0000000..fd9606c --- /dev/null +++ b/Document/research-workflow.md @@ -0,0 +1,418 @@ +# 语言条件化心理测量协调模型：研究工作流 + +版本：1.0｜日期：2026-09-20｜状态：工作流设计完成，研究执行尚未开始。 + +## 1. 目标与设计依据 + +本流程依据同目录下的《项目的正式定位》和《language-conditioned-psychometric-harmonization-model》设计。用户所称 `/Documents` 在当前项目中对应 `E:\Model\Document`。 + +核心目标是：用官方题目语义、调查背景与真实回答，估计题目参数、差异项目功能（DIF）、可比较的潜在表型及其不确定性；检验这种映射能否迁移到未见题目、问卷版本和国家。 + +优先检验的研究假设：在相同回答数据、调查设计处理和测试划分下，题目语义相对于无文本层级模型及人工构念标签，可以改善跨域测量与校准。研究必须允许该假设不成立；完成工作流不等于保证模型优于基线或达到发表标准。 + +第一版称为 **language-conditioned psychometric harmonization model**。只有获得充分的跨构念、跨数据集和跨任务迁移证据，再讨论 foundation model 的称谓。 + +本次完整阅读了两份项目文件，浏览了当前会话的全部技能目录，重点核对了学术研究、论文、审稿、SEM/IRT 与 PyMC 技能的职责和适用流程；同时抽查数据说明、质量报告及 NSDUH 构念映射表。尚未逐行审计个体数据、复核所有官方问卷或拟合模型。下文的“现有报告”与“建议验收条件”均不能当作本次重新计算的结果。 + +## 2. 当前材料如何影响路线 + +| 已观察到的材料 | 当前证据及限制 | 工作流中的处理 | +|---|---|---| +| 两份项目定位文件 | 目标清楚；详细文件中的若干数学公式存在转换缺损 | 以自然语言要求为依据，在模型规格中重新写清公式和识别约束 | +| GSHS 质量报告 | 现有报告记载 673,499 行、165 项调查、191 个非重叠组件，另有 10 项调查需范围复核；本次未重算 | 主开发候选；先核对组件、适用总体、官方文本、设计变量和题目共现 | +| GSHS 变量说明 | 同名 `raw_` 列不保证同义；权重、PSU、stratum 是待确认的候选字段 | 建立调查特异的题目版本 ID 与设计字典，不能直接按列名合并 | +| YRBS 全国原始库 | README 记载 1991–2023 年原始文件 | 原始文件存在与可直接分析是两个状态 | +| NSDUH/YRBS 整理包 | README 表明 YRBS 可分析表覆盖 1991–2019；2021、2023 等待导入布局验证 | 先审计是否已有后续修复；若没有，按官方导入程序验证后再解封 | +| NSDUH 构念映射 | 成人题、青少年题、COVID 条件题及派生变量混在候选表中；部分行没有原问题 | 回到完整字典及问卷核对青少年模块，不以 selected 表作为最终题目全集 | +| NSDUH 标签示例 | 2021 年 `YUSUITHK`、`SUICTHNK` 的候选构念同时包含 ideation 与 attempt；`SUIPLANYR` 标题明确为成人题 | 这是需要人工核实的映射问题；自动构念标签不能直接成为训练真值 | +| WHO/World Bank 背景表 | README 说明部分指标稀疏，WHO 资源数据不是完整年度面板 | 在题目模型建立后再加入；按可用时间匹配，避免未来信息泄漏 | +| LLM Agent 论文目录 | 从文件名看主要涉及规划、工具调用与多智能体 | 可供后续工程参考；本次未全文阅读，不能充当心理测量方法的核心文献依据 | + +CDC 当前页面列出了 2021、2023 年的 SAS 导入及格式程序，可作为修复本地 YRBS 导入状态的来源；是否与本地文件匹配仍需验证。[CDC 年度数据与文档](https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html) + +## 3. 总体流程和分支 + +```mermaid +flowchart TD + A[目标与数据版本登记] --> B[官方题库与回答字典] + B --> C[可识别性与题目多样性审计] + C --> D{证据支持哪条路线} + D -->|连接充分且可识别| E[IRT / 层级测量模型] + D -->|题目少或阶段结构更合适| F[多结局 / 潜类别 / 阶段模型] + D -->|缺乏可靠连接| G[直接协调与分调查分析] + E --> H[冻结划分与模拟验证] + F --> H + G --> H + H --> I[直接协调与传统基线] + I --> J[题目语义增益实验] + J --> K{是否支持语义模型} + K -->|支持| L[语言条件化模型与少样本适配] + K -->|不足| M[补充桥接数据或形成边界性结果] + L --> N[外部验证与完整消融] + M --> N + N --> O[可复现成果与论文审查] +``` + +文献定位可与题库整理同时推进；模型开发必须等待相应数据与识别条件满足。技术验收通过后，在已授权的执行范围内继续，不把每次常规检查变成用户确认。 + +## 4. 阶段 0：冻结研究问题、范围与版本 + +**目的：** 明确哪些证据能够支持核心主张，以及数据适用总体。 + +工作： + +1. 建立文件清单、来源、哈希、重复及组件关系，选定唯一分析入口；保留原始文件。 +2. 将 GSHS 设为跨国开发候选，YRBS 设为问卷及时间迁移候选，NSDUH 青少年模块设为外部候选。实际角色以审计结果为准。 +3. 定义年龄、在校状态、地域覆盖和调查年份。主分析优先限定在能够可靠识别的共同年龄与总体范围；无法统一的总体分层报告，不把学校样本直接外推到全部青少年。 +4. 分开记录测量构念：意念、计划、尝试、自伤、悲伤/绝望。后两者是否进入共同测量模型必须有理论和数据支持。 +5. 将欺凌、孤独、睡眠、物质使用、家庭与同伴支持、暴力暴露放在解释层。解释层首先报告关联，不能凭横断面数据得出因果或个体纵向预测结论。 +6. 进行聚焦文献定位：跨调查协调、语义预测 IRT 参数、跨语言 DIF、复杂抽样下的层级模型、新题目校准。建立与最接近方法的贡献比较表，不能预设“首创”。 +7. 检查实际运行环境、软件版本、可用内存/GPU、存储、嵌入获取方式和许可；本流程不预设已经具备全部依赖。 + +**技能：** `deep-research` 的文献与方法设计模块；`self-improving-agent` 记录已验证的工程问题。 + +**交付物：** `project_charter.md`、`data_manifest.csv`、`literature_matrix.csv`、`environment_audit.md`。 + +**Gate 0：** 有明确的目标总体、主要迁移任务、数据来源及待验证列表。文献不必无限扩展，先覆盖支撑研究定位与识别方案的关键问题。 + +## 5. 阶段 1：建立有来源证据的题库和回答层 + +**目的：** 把原始调查转为可以审计的测量资料。 + +### 5.1 必要的数据实体 + +| 实体 | 最少字段 | +|---|---| +| 调查/组件 | `survey_id, component_id, country, year, scope, population, language, questionnaire_version` | +| 题目实例 | `item_version_id, item_family_id, source_variable, question_text, construct, time_window, response_type, language` | +| 回答字典 | `item_version_id, raw_code, raw_label, canonical_code, missing_type, derivation_rule` | +| 回答长表 | `respondent_id, survey_id, component_id, item_version_id, raw_response, canonical_response, eligibility, observed_status` | +| 抽样设计 | `survey_id, weight_field, psu_field, stratum_field, variance_method, design_source` | +| 来源和审核 | `source_file, source_url, page_or_section, text_hash, reviewer_1, reviewer_2, review_status` | +| 候选连接 | `item_pair, relation_type, semantic_evidence, population_overlap, anchor_status, exclusion_reason` | + +`item_family_id` 连接同一题目的改写、翻译和版本；`item_version_id` 区分实际措辞、时间窗口、答案选项和适用条件。调查中的题目实例另保留来源身份。原始回答、派生指标、插补版本不得伪装为多个独立题目。 + +长表是保存回答和缺失状态的主格式；按调查生成稀疏的 person × item 矩阵及共现矩阵，避免创建大而稀疏的全库密集矩阵。 + +### 5.2 必须处理的事项 + +- 官方问题、答案标签、年龄范围和跳题逻辑逐项核对；只获得变量标题时标为待核实，不补写成“官方原文”。 +- 官方译文、实际施测语言、研究者翻译分别记录；仅有英语参考问卷不能证明实际以英语施测。 +- 区分未施测、不适用、合法跳题、拒答、不知道、普通缺失和真实阴性。合法跳题能否推导为阴性只能按明确的官方规则处理。 +- 主要结局由两名实际审核者复核并记录；AI 的两次检查可作预审，不能登记成两名人类审核者。尚无人类复核的条目标为 provisional，先用于流程开发。 +- 主要结局不能仅从自动筛选的 selected 表收集，应与完整字典核对，防止遗漏尝试次数等关键字段。 +- 多调查合并时为 PSU/stratum 增加调查或组件命名空间，并按官方设计保留必要关联，防止相同编号误合并。 +- YRBS 的原始文件、导入程序、标签、样本数和官方描述性结果需相互核对后才标为 ready。 + +**技能：** 执行时按需加载 `pdf`、`documents:documents` 读取问卷，`spreadsheets:Spreadsheets` 生成供审核的字典。本阶段仍需要项目专用的数据解析和检查代码；文档技能本身不执行统计协调。 + +**交付物：** `item_bank.csv`、`response_dictionary.csv`、`survey_design.csv`、`construct_ontology.yaml`、`responses_long.parquet`、`item_links.csv`、`source_conflicts.csv`。 + +**Gate 1：** 正式分析题目均有来源、编码、总体及缺失规则；主要结局完成所要求的双重核查。待核实题目保留在候选库，不能静默进入确认性分析。 + +## 6. 阶段 2：可行性、可识别性和连接审计 + +**这是全项目最先应完成的科学交付。** + +按国家 × 年份 × 工具 × 组件 × 构念统计：有效题目数、共同作答人数、阳性事件数、答案分布、缺失/跳题比例、设计变量完整性，以及考虑设计后的精度指标。权重推导的有效样本量可作筛查，但不能替代完整抽样设计方差。 + +同时生成： + +- person × item 回答矩阵及 item × item 共现矩阵； +- 调查—题目二部图、候选锚题图、连通分量和薄弱桥接点； +- 去重后的题目家族数、措辞版本数、语言数、时间窗口与回答形式多样性； +- 国家、工具、语言、年份与样本人群的混杂/秩亏表； +- 跨组件重复或重叠样本风险表。 + +### 6.1 审计决策 + +| 路线 | 需要的证据 | 不足时如何处理 | +|---|---|---| +| IRT / 多维测量 | 理论支持、实际共答、足够回答变化、明确标度与识别约束，且参数恢复可靠 | 简化维度、限制参数、改用其他观测模型或直接协调 | +| 层级跨组链接 | 可靠的锚定/桥接及足够连接，潜在均值与 DIF 可被区分 | 分连通分量分析；增加桥接样本；将无法识别的部分标为模型外推 | +| 阶段/潜类别/多结局 | 相应结构有证据支持 | 不强制假设“意念→计划→尝试”严格嵌套，检查无计划尝试等回答 | +| 直接二分类/频数协调 | 官方定义、时间范围、选项和总体足够相近 | 保留多个定义层级或分调查报告，不强行二元合并 | + +语义相近只产生候选锚题，不证明测量不变性；图连通也是必要线索而非充分的可识别性证明。允许少数部分不变锚题、锚题替换和留锚敏感性分析，但选择只能依据训练/开发资料。 + +国家潜在均值、国家题目阈值平移和无约束 DIF 可能混淆。模型规格必须写明参考组/标度、锚题约束及 DIF 约束。跨调查没有经验连接时，语义先验不能自动证明共同标度被数据识别。 + +**特别注意：** 语义到题目参数的学习，独立信息单位主要是题目家族与版本。受访者数量大不能弥补题目种类少。阈值不凭经验随意定为“每组至少多少人”，而应结合预期精度、设计效应、事件稀疏度和模拟参数恢复制定。 + +**技能：** `alterlab-sem-psychometrics`、`pymc-modeling`。前者引用的 `alterlab-ssci-measurement-gate`、`alterlab-statistical-analysis` 和 `alterlab-ssci-inference-gate` 不在当前可用技能目录；不能将这些依赖写成已经执行。其职责在本项目中转为显式审计清单和代码检查。 + +**交付物：** `feasibility_audit.md`、`construct_feasibility.csv`、`item_cooccurrence.parquet`、`anchor_graph.graphml`、`identification_spec.md`、`item_diversity_report.md`。 + +**Gate 2：** 每个构念得到“可进入潜变量模型 / 有条件可进入 / 仅可直接协调 / 暂不纳入”的判定与依据。最小可行实验的调查、构念及题目集合在此确定。 + +## 7. 阶段 3：冻结验证协议并做已知真值模拟 + +**目的：** 在查看最终测试表现前，定义成功、失败和计算可靠性的含义。 + +### 7.1 外层测试任务 + +| 任务 | 训练时必须留出什么 | 测试时允许使用什么 | +|---|---|---| +| 未见题目家族 | 同一家族所有目标回答与监督信号；训练不能混入其翻译/近重复版本 | 新题目正文、选项、时间窗口及预先允许的背景 | +| 未见问卷版本 | 指定版本及泄漏等价版本 | 版本元数据与题目文本；few-shot 时另给支持集 | +| 未见国家 | 该国全部年份和组件的回答 | 预先允许的国家背景；未知国家效应从层级分布预测并传播不确定性 | +| 未见地区 | 整个预定地区的回答 | 与未见国家任务一致；国家/地区定义预先冻结 | +| 时间外 | 截点后的回答和后验信息 | 截点前训练资料；背景变量按预测时点可得性匹配 | +| 跨工具 | 整个目标工具的回答 | 经总体与定义审计后的外部问卷信息 | + +未见题目家族与已知家族的新措辞是不同难度的任务，分别报告。所有预处理、降维、文本模型选择、先验调优和锚题筛选在内层训练/验证中完成。 + +Zero-shot 明确指“不使用目标题目/目标域的回答来拟合其参数”。对测试受访者使用其他允许的回答推断 θ 时，必须遮蔽正在评分的回答；没有可用回答时，对 θ 的预测分布积分。不能用某个回答推断 θ 后又对同一回答报 held-out 得分。 + +Few-shot 将目标域分成支持集和查询集；按 PSU 或独立受访者分离，依据实际设计避免聚类泄漏。可预设 0、25、100、500 个支持受访者的学习曲线，具体预算由审计和抽样单元决定，这些数值是候选实验设置而非样本充分性标准。 + +### 7.2 模拟实验 + +用已知参数生成：无 DIF/弱 DIF/强 DIF、少锚题、稀有事件、不同维度、结构性缺失与跳题、组间均值不同、抽样权重差异、国家与语言混杂等场景。 + +检查 a、b、DIF、群体分布及区间覆盖；必要时进行 scoped SBC。SBC 检验推断算法在生成模型下的校准，不证明真实数据符合模型。调查加权伪后验还需另做设计型重复抽样评估，不能直接套用普通生成模型的 SBC 作为充分证据。[Stan：Simulation-Based Calibration Checking](https://mc-stan.org/docs/stan-users-guide/simulation-based-calibration.html) + +真实数据的完整样本 IRT 参数不是已知真值，只能是带不确定性的参照。真实数据参数比较需保持共同标度；对齐不能使用已留出题目的真值信息。 + +**交付物：** `analysis_protocol.md`、`split_manifest.csv`、`simulation_plan.yaml`、`simulation_report.md`、`metrics_spec.yaml`。 + +**Gate 3：** 测试集身份、允许信息、主指标及报告方式冻结；简化模型能够在已知真值场景中可靠恢复。存在不可识别或计算问题时先修复，不通过提高模型复杂度掩盖问题。 + +## 8. 阶段 4：建立直接协调和传统基线 + +采用两条相互关联但不混淆的基线轨道。 + +| 轨道 | 模型 | 作用 | +|---|---|---| +| 测量 M0 | 官方定义下的直接协调 | 结局定义、设计加权比例及不确定性的基线；不能虚构其没有的 θ 或 a/b | +| 测量 M1 | Rasch/2PL/有序 IRT 或审计通过的替代形式 | 不使用语义的测量参照 | +| 测量 M2 | 层级 IRT/层级替代模型，有限 DIF | 检验层级共享本身能带来的迁移改善 | +| 预测 P0/P1 | Logistic、XGBoost；TabPFN 可选 | 检验一般表格预测能否解释附加收益，属于辅助任务 | +| 语义辅助 P2 | 嵌入 + 普通分类/回归模型 | 区分“文本对预测有帮助”和“文本对测量参数迁移有帮助” | + +传统 item-ID 模型无法 zero-shot 生成新题目参数时，明确标为不支持；另提供构念均值/层级先验预测作为可计算的冷启动基线，并报告 few-shot 曲线。不同模型只比较共同支持的估计目标。 + +先验证加权描述性结果与同一总体、同一分母下的官方报告相容；不能通过调整编码“对齐”不匹配的官方总体。 + +复杂抽样处理分为三层：描述性估计及设计方差；模型内声明权重归一化和伪似然等选择；对关键群体估计使用合适的复制权重或设计一致的重抽样检查。权重直接乘进似然不等于区间已经正确。跨国训练权重与各调查群体估计权重分别定义。 + +**技能：** `alterlab-sem-psychometrics`、`pymc-modeling`，以及项目专用复杂抽样代码。SEM 技能中的连续指标 CFA 模板、omega 简式和拟合经验阈值不能不加判断地用于二元、稀有或多维条目。 + +**交付物：** `baseline_results.parquet`、`design_validation.md`、`model_diagnostics.md`、基线参数和预测文件。 + +**Gate 4：** 基线可复现，设计变量保留，主要参数收敛/精度和后验预测检查合格，评估未发生回答或调查泄漏。 + +## 9. 阶段 5：低成本语义增益实验 + +先回答“文本是否包含可迁移的测量信息”，再训练完整模型。 + +1. 分别编码题目正文、时间窗口、选项、整体模板；语言/国家/工具背景单独保存。 +2. 比较可复现的开源多语言嵌入、高性能通用嵌入、领域嵌入；具体模型在执行时按许可、可得性、成本及训练内性能确定并冻结。 +3. 固定编码器起步，采用低维/正则化映射。文本样本少时不默认微调大模型。 +4. 只用训练折拟合题目参数或相关结构，再评估语义是否解释这些测量属性；用参数后验样本或误差模型传播估计不确定性，不能把拟合参数当无误差标签。 +5. 对 item–item correlation 的比较使用共同作答证据，并考虑国家与总体差异；同国相关结构相似不一定是题目语义造成的。 + +**最低限度对照：** 人工构念标签；item-ID；TF–IDF；随机向量；在题目之间打乱嵌入对应关系；打乱词序；删除时间窗口；删除回答标签；单语/多语对照；只有背景无题目文本的模型。 + +词序打乱并不是严格无语义对照，因为部分编码器仍可保留词义；题目—嵌入对应关系置乱更直接检验监督关系。全部置乱遵守题目家族分组，避免只扰动部分重复版本。 + +**交付物：** `item_embeddings.parquet`、`embedding_manifest.json`、`semantic_increment_report.md`、`semantic_controls.parquet`。 + +**Gate 5：** 依据预先冻结的跨题目家族指标、效应区间与实际意义判定增益，不能只凭 UMAP 图或训练相关系数。 + +若语义没有稳定增益：先检查题目多样性、文本/编码错误和任务天花板；扩充真实题目或桥接资料后重新开展明示为新版本的研究，或报告语义增益的适用边界。不能在最终测试集上反复挑模型。 + +## 10. 阶段 6：语言条件化层级测量模型 + +第一版只选审计通过的构念和观测形式。二元模型的示意为： + +\[ +Y_{ijg}\sim\mathrm{Bernoulli}\{\operatorname{logit}^{-1}[a_j(\theta_{ig}-b_j-\delta_{jg})]\}, +\] + +\[ +\log a_j=\alpha_a+f_a(e_j)+r_{aj},\qquad +b_j=\alpha_b+f_b(e_j)+r_{bj}. +\] + +其中 e_j 来自题目语义，r 为有正则化的题目残差；δ 为有约束的组别 DIF。组别可由调查背景定义，但不能将完全混杂的国家、语言和工具效应当作分别可解释的因果效应。 + +这只是结构示意，实施前必须补齐 θ 的参考标度、锚题限制、残差及 DIF 先验、组间分布、权重方式和缺失/跳题的观测机制。有序题目使用有序阈值；不同构念经理论和数据支持后才增加维度，不直接将所有题目塞进单维模型。 + +开发顺序： + +1. **M3a：语义预测阈值。** 固定嵌入，以简单线性/低维映射起步。 +2. **M3b：语义同时预测区分度和阈值。** 与 M3a 比较，判断额外复杂度是否被数据支持。 +3. **M3c：加入有约束的语义 × 背景 DIF。** 与无交互或单纯层级 DIF 比较。 +4. **M4：新题目、新国家和 few-shot 适配。** 更新被允许的局部参数，报告支持集大小与成本；查询集保持封存。 + +新题目的残差、未见国家效应和 DIF 均需从后验预测分布抽取/积分，不能简单设为 0 后给出过窄区间。Zero-shot 的具体国家 DIF 是模型预测，不是已经从该国回答识别出的参数。 + +先在小规模可诊断样本上完成可靠推断，再评估扩展方式。大数据近似推断应在可运行的子集上与高质量采样结果比较。报告采样发散、R-hat、有效样本量、MCSE、先验/后验预测检查；诊断未解决不能作为确认性推断发布。 + +**技能：** `pymc-modeling` 为主要实现依据，`alterlab-sem-psychometrics` 提供测量检查。PyMC/ArviZ 等 API 按实际安装版本核对，不照搬技能文件的版本假设。 + +**交付物：** 模型代码、冻结配置、参数后验、预测文件、`model_card.md`、`adaptation_protocol.md`。 + +**Gate 6：** 计算可靠，复杂度有支持，新域区间包含未见效应的不确定性，且 M3/M4 的结果可与相应传统基线公平比较。 + +## 11. 阶段 7：最终验证、消融和主张判定 + +### 11.1 指标层次 + +| 层次 | 指标与解释 | +|---|---| +| 共同主指标 | 未见题目家族的 held-out log score；未见国家/工具的预先指定共同目标上的校准或分布误差。对每个主张预先指定指标，不能挑表现最好的域 | +| 模拟确认 | 已知真值下 a/b/DIF 及群体量的恢复误差，预设区间的重复覆盖率 | +| 真实数据测量 | 后验预测、局部依赖、DIF 效应与不确定性、锚题敏感性；可靠性指标按模型类型报告 | +| 群体估计 | 设计加权比例/分位数、标准误、共同定义下的差异；参考比例也有抽样误差 | +| 辅助预测 | Brier、校准截距/斜率、AUPRC、AUROC；不以 AUC 单独证明测量协调成功 | +| 迁移成本 | 支持集规模、计算时间、内存、适配稳定性 | + +θ 的任意阈值不能自动叫作患病率。没有经验证的诊断阈值时，报告潜在分布、分位数或指定参考题目上的模型推导阳性比例。 + +真实数据潜变量与参数没有普遍已知的真值，因此不能宣称已经测得“真实 θ 区间覆盖率”。真实数据依靠独立查询回答、设计估计及敏感性分析；参数覆盖率主要由模拟支撑。 + +### 11.2 最低限度消融 + +- M0 → M1 → M2 → M3a → M3b → M3c → M4，区分每项增益来源。 +- 去题目正文、时间窗口、答案标签；去国家/年份背景。 +- 无文本、构念标签、随机/置乱嵌入与背景专用模型。 +- 无 DIF、替换/删除锚题、删除一个调查工具、排除低频题目。 +- 加权与不加权、不同合理权重缩放；PSU/stratum 完整与受限样本。 +- 严格同定义题目与较宽协调定义；缺失/跳题的合理替代假设。 +- 固定嵌入与微调仅在有效题目量足够时比较。 + +按国家/调查等外层独立单元报告配对差异和区间，避免把大量相关回答当作独立的迁移重复。以预先定义的国家宏平均为主时，另报告样本加权结果；国家数少则明确精度有限。 + +亚组按性别、年龄、地区、语言、收入组及可靠可用的其他字段评估。测量 DIF 与预测性能差异分开；本研究没有临床筛选或资源分配决策时，不额外声称已验证政策公平性。 + +**成功判定：** 语义模型在预设迁移任务上相对最佳适用传统基线具有稳定且有实际意义的改善，同时校准/区间质量没有重要退化，并经关键消融支持。最小有意义改善和非劣界限在开发/模拟阶段设定并冻结。 + +某个任务失败时限制对应主张。例如未见国家改善而未见题目不改善，只能支持相应范围的迁移结论。完整验证流程即使得到阴性结果也可形成有价值的基准或方法边界成果。 + +**交付物：** `external_validation_report.md`、`ablation_results.parquet`、`subgroup_results.parquet`、`claims_evidence_matrix.csv`、最终表图。 + +## 12. 阶段 8：可复现交付与论文审查 + +先冻结分析版本，再把结果转换为论文。可提前写引言和方法草稿，结果与结论必须来自已冻结输出。 + +1. `academic-paper`：构建“研究问题—方法—证据—限制”的论证结构。 +2. 完整性审计：核查文献、正文主张、表图数字和分析文件对应关系，注明检查分母、抽样范围及未核实项。 +3. `academic-paper-reviewer`：先方法学审查，再完整审查；角色覆盖心理测量、调查抽样、跨文化迁移与机器学习。多角色 AI 意见不能替代人类专家验证。 +4. `academic-paper` revision：按问题清单修订；复审检查问题是否解决，之后再次进行完整性核查。 +5. 根据目标期刊使用 `apa-style` 或对应引用规范；`humanizer:humanizer` 只改善表达，不改变证据强度或隐藏 AI 使用记录。 +6. 按需用 `documents:documents`、`pdf`、`presentations:Presentations` 输出并检查版式；科研图表使用可复现绘图工具。 + +`academic-pipeline` 适合管理研究到论文的交付、审稿和状态记录。其默认“已有数据→写作”的路由不适合直接用于本项目，必须先经过上述数据、识别和实验门槛。本次仅设计流程，并未启动其完整执行状态机；若后续选择按原版执行，其中阶段确认机制须按用户授权和实际技能规则处理。 + +**最终成果包：** 可追溯题库、数据处理代码、冻结划分、模拟基准、传统与语义模型、参数/预测与区间、实验记录、数据/模型说明卡、论文及补充材料。能否分发原始或衍生个体数据按来源许可另行确认，默认优先交付代码和聚合结果。 + +## 13. 技能分工与真实缺口 + +| 技能/类别 | 使用位置 | 能力边界 | +|---|---|---| +| `deep-research` | 文献定位、方法与证据核验 | 不替代逐题官方字典核对和实际统计分析 | +| `alterlab-sem-psychometrics` | 测量结构、IRT、不变性与可靠性 | 不自带本项目的语义条件化模型或完整复杂抽样实现；若干上游技能未安装 | +| `pymc-modeling` | 分层建模、模拟、诊断、预测 | 不自动保证可识别性、设计一致性和计算可扩展性 | +| `academic-pipeline` | 阶段状态、交付、论文审查编排 | 学术生产管理层，需要接入本项目的实验工作流 | +| `academic-paper` / `academic-paper-reviewer` | 写作、审稿、修订、复核 | 不把模拟审稿当作方法或结果已获外部认可 | +| `pdf` / `documents` / `spreadsheets` | 官方文件提取、审核字典、成果输出 | 提取文字不等于完成官方语义确认 | +| `apa-style` / `humanizer` | 引用与文字质量 | 不生成证据，不提升未经支持的科学主张 | +| `visualize` / `presentations` | 后续展示进展和模型机制 | 科研结果图另用标准可复现绘图；本次用静态流程图已足够 | +| `self-improving-agent` | 记录复现环境和错误修复 | 仅沉淀已核实经验，不作为研究证据 | +| `skill-creator` | 流程跑通后可封装项目技能 | 本次先交付工作流，不提前把未验证流程固化成技能 | + +其余网站、插件管理、图像生成、应用控制及来源标记移除等技能不进入核心研究路线。当前缺口主要是项目专用的题库解析、锚定图审计、复杂抽样评估、无泄漏划分和语义条件化测量模型代码，不是缺少更多通用写作技能。 + +## 14. 实施批次、资源和停机条件 + +### 第一批：拿到可行性答案 + +完成阶段 0–2。优先交付八项：数据版本清单、主要结局来源表、总体/年龄适用表、共现矩阵、锚定图、题目多样性表、构念可行性判定、下一步最小实验定义。完成后才能给出可信的训练规模、硬件需求和工期估计。 + +### 第二批：最小科学闭环 + +从已通过审计的一个构念或明确的多结局结构起步，使用题目来源完整、设计资料完整的连通子集。完成一个已知真值模拟、M0/M1/M2、固定嵌入到阈值的简单映射，以及一个未见题目家族和一个未见国家任务。有效版本不足时如实报告，不能用随机个体划分替代。 + +### 第三批：扩展与正式交付 + +在最小实验和语义增益门槛通过后，再扩展到更多国家、年份、工具、DIF 和 few-shot。第二构念扩展放在主要 benchmark 稳定以后。 + +### 资源策略 + +先读取元数据和小规模样本做验证，不直接在全量受访者上拟合最复杂模型。嵌入按文本哈希和模型版本缓存；中间数据保存为列式表。实测单个基线、单个模拟和单个外层折的时间/内存后估算总预算，不在未审计前承诺固定完成天数。 + +### 停机或降级条件 + +| 条件 | 动作 | +|---|---| +| 官方文本、适用总体或编码无法确认 | 题目暂不进入正式分析，继续可确认部分 | +| 锚定不足、图不连通或标度不可识别 | 分组件报告、直接协调或提出桥接数据方案 | +| 独立题目家族太少 | 简化语义映射并限制泛化主张，优先寻找真实题目多样性 | +| 收敛或参数恢复失败 | 简化/重新参数化，保留失败记录，不发布不可靠推断 | +| 语义收益只出现在随机个体划分 | 判定核心迁移主张未获支持 | +| 最终测试反复被用于调参 | 将该测试集降为开发集，并重新确定独立测试证据 | +| 权重/PSU/stratum 不完整 | 标记推断限制，采用有依据的可用设计方法，不宣称完整设计校正 | + +## 15. 文件与状态管理约定 + +建议执行时在项目中新增以下目录；它们在本次尚未创建： + +```text +E:\Model\ + Document\research-workflow.md + research\protocol\ 研究问题、识别规格、验证协议 + research\audit\ 题库来源、连接审计、可行性报告 + research\metadata\ 题库、字典、构念本体 + research\processed\ 派生回答表与矩阵 + research\splits\ 冻结测试划分 + research\embeddings\ 版本化嵌入与清单 + research\models\ 基线与语义模型 + research\experiments\ 模拟、配置、诊断、运行日志 + research\results\ 已冻结结果与表图 + research\manuscript\ 论文、审稿及修订记录 +``` + +每个阶段记录：`status, input_hashes, config_version, outputs, checks, unresolved_issues, next_action`。状态使用 `not_started / in_progress / passed / conditional / blocked / failed`；只有交付物和检查证据满足要求才标为 passed。证据不足时可以完成一个“有条件”或“不可行”的审计结论，不能把它记为模型已经验证。 + +每次新任务只加载本流程、阶段状态和当前所需输入，避免重复全文读取整个资料库。若后续启用多代理，按官方题库、测量方法和验证审计等清晰边界分工，交接文件并记录来源；多代理不是本工作流成立的必要条件。 + +## 16. 本次查阅的主要依据 + +本地研究目标： + +- [项目的正式定位](E:/Model/Document/项目的正式定位.md) +- [模型定位与输入输出](E:/Model/Document/language-conditioned-psychometric-harmonization-model.md) + +本地数据状态： + +- [整理包说明](E:/Model/Dataset/可直接分析数据包_NSDUH_YRBS/README.md) +- [候选构念映射](E:/Model/Dataset/可直接分析数据包_NSDUH_YRBS/harmonization/construct_crosswalk.csv) +- [GSHS 变量注意事项](E:/Model/Dataset/GSHS-全球学生健康调查数据/GSHS/02_documentation/变量使用注意事项.md) +- [GSHS 现有质量报告](E:/Model/Dataset/GSHS-全球学生健康调查数据/GSHS/04_quality_control/数据质量检查摘要.md) +- [YRBS 原始库说明](E:/Model/Dataset/YRBS_National_1991_2023/README.md) +- [国家背景资料说明](E:/Model/Dataset/WHO-country_context/README.md) + +关键技能： + +- [academic-pipeline](C:/Users/Jino/.codex/skills/academic-pipeline/SKILL.md) +- [deep-research](C:/Users/Jino/.codex/skills/deep-research/SKILL.md) +- [SEM 与心理测量](C:/Users/Jino/.codex/skills/alterlab-sem-psychometrics/SKILL.md) +- [PyMC 建模](C:/Users/Jino/.codex/skills/pymc-modeling/SKILL.md) +- [academic-paper](C:/Users/Jino/.codex/skills/academic-paper/SKILL.md) +- [academic-paper-reviewer](C:/Users/Jino/.codex/skills/academic-paper-reviewer/SKILL.md) + +官方技术来源： + +- [CDC 年度 YRBS 数据与导入文档](https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html)：用于确认 2021/2023 官方导入资料入口存在。 +- [Stan SBC 指南](https://mc-stan.org/docs/stan-users-guide/simulation-based-calibration.html)：用于推断计算验证的设计；并非真实数据可识别性的替代证据。 + +以上为针对现有项目材料提出的工作流，不是已完成的数据审计或统计研究报告。 + +## 17. GitHub 组件选型补充（2026-09-20） + +已完成首轮外部仓库检索与静态检查，详见 [GitHub 借用评估](E:/Model/Document/github-reuse-assessment.md) 和 [仓库版本登记表](E:/Model/Document/github-repository-registry.json)。优先试验 Harmony、Sentence Transformers、mirt、PyMC 与 R survey 的组合；Rmonize 作为可追溯协调流程的可选组件。 + +接入时先隔离 Harmony 与新版语义编码环境，遵守原有官方题库、可识别性和迁移验证门槛。仓库审阅不代表本地依赖已安装或统计验证已通过。建议后续将文档、代码、配置及数据哈希纳入本地 Git，原始个体数据和大型模型文件另行管理。 diff --git a/Document/项目的正式定位.md b/Document/项目的正式定位.md new file mode 100644 index 0000000..b17ec5d --- /dev/null +++ b/Document/项目的正式定位.md @@ -0,0 +1,162 @@ +A language-conditioned psychometric foundation model for harmonizing +adolescent suicide-risk phenotypes across countries and survey +instruments 中文： 用于跨国家、 +跨调查工具青少年自杀风险表型协调的语言条件化心理测量基础模 型 + +我们计划开发一个用于跨国家、 跨年份和跨调查工具协调青少年自杀相关表型的 +语言条件化心理测量模型。 项目不是简单比较XGBoost、 随机森林和深度学习的 +预测性能， 而是研究不同问卷措辞、 时间窗口、 +答案编码和文化背景是否测量同 一心理构念，以及 LLM +题目语义能否帮助恢复这种可比性。 +第一阶段请先完成模型可行性与可识别性审计。基于现有 GSHS、YRBS 和 NSDUH +数据， 建立受访者—题目回答矩阵、 题目共现矩阵和跨调查锚定题目表。 +逐个国家—年份统计自杀意念、 计划、 尝试、 自伤及悲伤/绝望的有效题目数、 +有 效样本数、 阳性事件数、 缺失机制和跳题条件。 判断哪些构念可以做IRT +或层级 潜变量模型， 哪些只能做直接二分类协调。 +不得仅根据变量名判断题意， 必须使 用已经确认的官方问题、答案标签和编码。 +请把欺凌、孤独、睡眠、物质使用、家庭支持、同伴支持和暴力暴露作为潜在表 +型的解释变量， +不要在没有理论依据的情况下与自杀行为题目共同组成一个量表。 +所有分析必须保留调查权重、PSU、stratum 和国家—年份标识。 +第二阶段建立直接协调、 传统IRT、 层级IRT、Logistic、XGBoost 及可选 +TabPFN 基线。 验证必须采用留一国家、 留一地区、 +留一问卷版本和时间外验证， 不能只 做随机个体划分。 主要评价包括held-out +likelihood、 校准、AUPRC、 加权患病率 误差、DIF 和不确定性覆盖。 +第三阶段使用多语言 LLM 对官方题目正文、 时间窗口和答案标签分别生成嵌入， +检验其能否解释经验题目相关结构和传统 IRT 题目参数。必须设置人工构念标 +签、传统文本特征、随机嵌入、打乱词序和删除时间窗口等对照。 +第四阶段开发语言条件化层级心理测量模型，使 LLM 嵌入预测题目区分度、阈 +值和跨国家 DIF，并实现对未见国家、未见问卷版本和新题目的不确定性推断。 +完整模型必须与传统方法进行消融比较。 + +总体目标 开发一个将官方问卷文本、 答案编码、 +调查背景和个体回答联合起来的模型， 使 不同国家、 +年份和调查工具测得的自杀相关表型具有可比较性， 并量化这种协调 + +结果的不确定性。 主要科学问题 1. 不同问卷中的自杀意念、 计划、 尝试、 +悲伤/绝望等题目， 是否测量相同的 潜在风险结构？ 2. +题目措辞、时间窗口和回答方式的变化，会造成多大的测量偏差？ 3. LLM +获得的题目语义表示，能否预测题目的难度、区分度、阈值及跨文 化偏移？ 4. +加入题目语义后， 能否提升对未见国家、 未见年份和未见问卷版本的表型 +协调能力？ 5. 哪些题目在国家、 性别、 年龄、 +调查年份之间存在差异项目功能， 即DIF？ 6. +协调后的自杀风险表型，是否比简单的 0/1 变量更稳定、更可迁移？ +二、需要先澄清的关键点 这个项目不能一开始就假设所有变量都能做 IRT。 经典 +IRT 通常要求： • 同一受访者回答多个测量同一潜变量的条目； • +不同版本之间存在共同锚定条目； • 每个条目有足够的阳性回答； • +调查间存在可建立连接的重叠题目。 因此，第一项任务必须是“可识别性审计”。 +如果多数受访者只有一条自杀意念题、 一个计划题和一个尝试题， 那么可以构建 +“自杀行为严重程度/阶段”模型，但不能轻易声称建立了高精度临床潜变量量表。 +此外， 不建议把欺凌、 孤独、 +物质使用和家庭支持全部当作“自杀风险量表”的题 目。更合理的结构是： +测量层： 意念、计划、尝试、自伤、悲伤/绝望 ↓ 自杀相关潜在表型 + +解释层： 欺凌、孤独、睡眠、物质使用、家庭支持、 +同伴支持、暴力暴露、国家社会环境 ↓ 解释潜在表型的差异及异质性 +这样测量模型和病因/解释模型不会混淆。 + +三、具体研究目标 Aim 1：建立跨调查的标准化题目知识库 对 GSHS、YRBS 和 +NSDUH 相关题目建立机器可读知识库。 每个题目至少包含： • 官方原文； • +如能获得，调查实施语言和译文； • 变量名； • 构念； • 时间窗口； • +行为对象； • 回答类型； • 原始答案标签； • 原始编码； • 缺失值类型； • +国家； • 年份； • 问卷版本； • 调查工具； • 适用人群和跳题条件； • +weight、PSU、stratum； + +• 官方来源； • 人工确认状态。 交付物： • item_bank.csv • +response_dictionary.csv • survey_design.csv • construct_ontology.yaml • +数据字典和质量报告 验收要求： • 进入正式模型的题目必须有官方文本； • +不得根据变量名猜测题意； • 每个协调变量必须能追溯到官方来源； • +主要结局变量人工双重核查； • 记录冲突和无法确认的变量，不强行填充。 Aim +2：建立语义表示和问卷本体 LLM 输入内容 +不能只输入问题正文。建议将一个题目表示为： Construct: suicidal ideation +Question: During the past 12 months, did you ever seriously consider +attempting suicide? Time window: past 12 months Response options: Yes; +No Population: school-attending adolescents Survey: GSHS Country: … +Year: … Language: English 分别编码： + +• 问题正文嵌入； • 时间窗口嵌入； • 回答标签嵌入； • 调查工具嵌入； • +国家语言背景嵌入； • 题目整体模板嵌入。 至少比较三类嵌入 1. +开源多语言模型； 2. 通用商业或高性能文本嵌入模型； 3. +精神健康领域或医学文本嵌入模型。 +模型名称不应提前写死，以实际可复现性、许可证和数据合规性确定。 +必须设置的对照 • 人工构念标签； • TF–IDF 或传统文本特征； • +不使用文本、只使用题目 ID； • 随机嵌入； • 打乱词序； • 删除时间窗口； • +删除回答标签； • 单语模型与多语言模型。 交付物： • +item_embeddings.parquet • 题目相似度矩阵； • UMAP 或聚类图； • +语义聚类与人工构念的一致性结果； • 不同嵌入模型的稳定性报告。 Aim +3：开发语言条件化心理测量模型 + +建议先建立五级模型体系，不要直接开发最终复杂模型。 M0：直接协调基线 +按官方定义将题目转化为： • 自杀意念：0/1； • 自杀计划：0/1； • +自杀尝试：0/1 或次数； • 悲伤/绝望：0/1； • 自伤：0/1 或频率。 +这是所有高级模型必须超越的基线。 M1：传统心理测量模型 根据数据结构选择： +• binary/graded-response IRT； • two-parameter logistic IRT； • +continuation-ratio model； • latent class analysis； • bifactor model； +• item-factor model。 模型示意： \[ P(Y\_{ij}=1)=^{-1} {a_j(*i-b_j)} \] +其中： • (Y*{ij})：个体 (i) 对题目 (j) 的回答； • +(*i)：个体自杀相关潜在表型； • (a_j)：题目区分度； • +(b_j)：题目阈值或难度。 M2：层级跨国模型 +允许题目参数随国家、年份和调查工具变化： \[ +b*{jcy}=b_j+u_c+v_y+w\_{survey}+\_{jcy} \] + +其中： • (u_c)：国家效应； • (v_y)：年份效应； • +(w\_{survey})：调查工具效应； • (*{jcy})：题目在特定国家—年份的 DIF。 +必须防止国家、 年份和调查工具完全共线。 无法分离时要降低模型复杂度或改变 +参数化方式。 M3：语言条件化模型 让 LLM 语义嵌入预测题目参数： \[ a_j= +{f_a(e_j)} \]\[ b_j=f_b(e_j) \]\[* {jc}=f\_{}(e_j,z_c) \] 其中： • +(e_j)：题目文本、时间窗口和答案标签的语义嵌入； • +(z_c)：国家语言及社会环境特征； • +(f_a,f_b,f\_{})：神经网络或贝叶斯函数。 这是项目的核心算法创新。 +M4：不确定性与新调查适配 +对未见过的新题目，只提供其文本和少量回答，即可估计： • +题目属于什么构念； • 可能的题目参数； • 与既有题目的等值关系； • +潜在表型分布； • 预测区间； • 是否超出模型适用范围。 可以使用： • +Bayesian neural network； • deep ensemble； + +• variational inference； • conformal calibration； • hierarchical +shrinkage； • few-shot/in-context adaptation。 “foundation +model”的称谓需要谨慎： 只有当模型能跨构念、 跨数据集和跨任务迁 +移时才适合使用。如果只处理几项自杀题，建议称为： Language-conditioned +psychometric model 这在审稿中会更稳妥。 + +四、模型训练和验证设计 1. 不能随机划分全部个体 随机划分会让同一国家、 +同一问卷版本同时出现在训练集和测试集， 容易产生虚 高结果。 必须包括： • +留一国家验证； • 留一 WHO 地区验证； • 留一调查版本验证； • +早期年份训练、晚期年份测试； • GSHS 训练、YRBS/NSDUH 外部验证； • +低收入国家与高收入国家交叉验证。 2. 三种核心验证任务 Task +A：未见国家迁移 训练时完全排除某个国家，再评价模型能否恢复该国： • +题目参数； • 自杀表型分布； • 加权患病率； • 风险因素排序； + +• 性别差异。 Task B：未见问卷版本迁移 +训练集中不出现某种题目措辞或回答编码，仅用官方题目文本推断新条目参数。 +这是最能体现 LLM 价值的任务。 Task C：跨调查工具迁移 • GSHS 训练； • +YRBS 验证； • NSDUH 青少年样本验证。 +必须先确定目标表型的定义确实可对应，不能直接比较未经协调的发生率。 3. +评价指标 测量质量 • posterior predictive checks； • item characteristic +curves； • test information； • empirical 与预测 item–item correlation； +• factor congruence； • measurement invariance； • DIF 检出率及效应量； +• latent score reliability。 泛化质量 • held-out item log-likelihood； • +held-out country log-likelihood； • Brier score； • AUROC 和 AUPRC； • +calibration slope/intercept； • expected calibration error； • +prevalence estimation error； + +• country-level rank correlation； • uncertainty coverage。 公平性 +至少按以下亚组报告： • 性别； • 年龄； • 地区； • 国家收入组； • +调查语言； • 城乡——如果数据存在； • 边缘化群体——仅在变量可靠时使用。 +需要区分： • 测量公平性：同样潜在风险是否产生相似回答概率； • +预测公平性：模型在不同群体上的性能是否一致； • +政策公平性：固定资源条件下谁被模型筛选出来。 4. 复杂抽样处理 +同事必须保留： • survey weights； • PSU； • stratum； • +国家—年份调查标识。 建议同时进行： 1. survey-weighted 描述性统计； 2. +pseudo-likelihood 或加权 Bayesian 模型； 3. PSU/stratum +稳健方差或重复权重近似； 4. 加权与不加权敏感性分析。 +不能因为神经网络不方便使用复杂抽样设计，就把设计变量删除。 + +五、最重要的消融实验 这是算法稿能否成立的关键。 至少比较： 模型 文本语义 +层级结构 DIF 调查设计 直接 0/1 协调 否 否 否 是 传统 IRT 否 否 部分 可选 +层级 IRT 否 是 是 是 LLM 嵌入＋普通分类器 是 否 否 否 语言条件化 IRT 是 +是 是 是 完整模型 是 是 是 是＋不确定性 额外消融： • 去除题目正文； • +去除时间窗口； • 去除答案标签； • 去除国家信息； • 去除年份； • +使用随机嵌入； • 替换不同 LLM； • 冻结与微调嵌入； • 删除低频题目； • +删除一个调查工具； • 不使用权重。 如果完整模型只提升随机划分 +AUC，却不能提升未见国家或未见题目的结果， 创新性就不够。 diff --git a/Progression.md b/Progression.md new file mode 100644 index 0000000..e214e72 --- /dev/null +++ b/Progression.md @@ -0,0 +1,625 @@ +# 项目进度档案 + +版本：1.2 +建立日期：2026-09-20 +当前总状态：`in_progress` +当前阶段：阶段 0 已完成并完成环境补强；下一行动进入阶段 1 + +## 1. 管理规则 + +本档案严格对照 [`Document/research-workflow.md`](Document/research-workflow.md) 维护。每次项目研究行动前必须完整读取工作流与本档案；行动完成后必须更新本档案。项目级强制规则同时记录于 [`AGENTS.md`](AGENTS.md)。 + +状态只使用：`not_started / in_progress / passed / conditional / blocked / failed`。 + +行动记录至少包含： + +- 时间与阶段 +- 动作及目的 +- 输入及其版本或哈希（适用时） +- 输出文件 +- 检查和证据 +- 未解决问题 +- 下一步 + +“已读取”“已设计”“已有报告称”与“已经重新验证”必须明确区分。未经本项目当前执行重新核验的结果不得升级为已验证证据。 + +## 2. 阶段状态 + +| 阶段 | 名称 | 状态 | 当前判定 | +|---|---|---|---| +| 0 | 冻结研究问题、范围与版本 | `passed` | 四项交付物齐备，Gate 0 已通过；研究工具链补强与锁定完成 | +| 1 | 有来源证据的题库和回答层 | `not_started` | 环境阻断已解除；下一行动启动阶段 1 | +| 2 | 可行性、可识别性和连接审计 | `not_started` | 等待 Gate 1 | +| 3 | 冻结验证协议与已知真值模拟 | `not_started` | 等待 Gate 2 | +| 4 | 直接协调和传统基线 | `not_started` | 等待 Gate 3 | +| 5 | 低成本语义增益实验 | `not_started` | 等待 Gate 4 | +| 6 | 语言条件化层级测量模型 | `not_started` | 等待 Gate 5 | +| 7 | 最终验证、消融和主张判定 | `not_started` | 等待 Gate 6 | +| 8 | 可复现交付与论文审查 | `not_started` | 等待阶段 7 的冻结结果 | + +## 3. 当前冻结方向 + +- 项目暂定名称：language-conditioned psychometric harmonization model。 +- 首要任务是阶段 0–2 的可行性和可识别性审计，不直接训练完整复杂模型。 +- GSHS 为跨国开发候选；YRBS 为问卷与时间迁移候选；NSDUH 青少年模块为外部候选。最终角色由审计决定。 +- 测量层暂分为自杀意念、计划、尝试、自伤、悲伤/绝望；解释变量不与结局题目无依据地混成单一量表。 +- 核心验证必须覆盖未见题目家族、未见问卷版本、未见国家/地区和时间外验证；随机个体划分不能作为核心证据。 +- 工作流允许阴性结果；“完成流程”不等于模型优于基线或达到发表标准。 + +## 4. 阶段 0 交付物状态 + +| 交付物 | 状态 | 路径/说明 | +|---|---|---| +| `project_charter.md` | `passed` | 已完成 [`research/protocol/project_charter.md`](research/protocol/project_charter.md)，冻结阶段 0–2 的研究问题、总体政策、构念边界、迁移任务、范围外事项与待验证清单 | +| `data_manifest.csv` | `passed` | 已完成 [`research/audit/data_manifest.csv`](research/audit/data_manifest.csv)，共 734 个文件；来源登记、项目角色、唯一候选入口、分析就绪与许可/分发状态已补充，并另有来源组与重复摘要 | +| `literature_matrix.csv` | `passed` | 已完成 [`research/protocol/literature_matrix.csv`](research/protocol/literature_matrix.csv)；13 条核验来源（11 条同行评审、2 条预印本），另有可读工作簿与定位综合 | +| `environment_audit.md` | `passed` | 已完成并更新 [`research/audit/environment_audit.md`](research/audit/environment_audit.md)；Python、R、复杂抽样、心理测量与 CUDA 工具链均已运行验收并锁定 | + +Gate 0 当前判定：`passed`。四项交付物齐备；目标总体政策、主要迁移任务、数据来源、范围边界和待验证列表均已明确。许可及数据版本问题已登记为后续显式验证项，不再是未记录的未知状态。 + +## 5. 已确认的本地事实 + +- 正确工作流路径为 `E:\Model\Document\research-workflow.md`；用户所称 `/Documents` 在本项目中对应单数目录 `Document`，文件名使用连字符。 +- `Dataset` 当前包含 734 个文件，总大小约 20.47 GB。 +- 已建立工作流建议的 `research/` 目录结构。 +- 已对 734 个数据文件计算 SHA-256，并生成逐文件清单；清单包含重复文件分组字段。 +- 数据清单已由单纯哈希表升级为来源登记：17 个数据组均有 authority、project role、canonical analysis entry、readiness 和 license/distribution 状态；117 个重复哈希组涉及 283 个文件、166 个额外副本，潜在冗余约 0.054 GiB。当前不删除任何副本。 +- GSHS 现有说明记录主表 673,499 行、637 列、165 项调查、191 个非重叠组件；这些数字目前属于既有质量报告陈述，尚未由本轮执行重算。 +- YRBS 原始库覆盖 1991–2023；现有整理包说明 1991–2019 已生成分析表，2021/2023 仍处于导入布局验证隔离状态。 +- 系统 Python 3.13.7、Pandoc 3.11、31.82 GiB RAM 和 NVIDIA GeForce RTX 5060 Ti 16 GB 已核验；正式项目环境使用 CPython 3.12.14、PyMC 6.3.2、R 4.6.1、mirt/lavaan/survey 栈及 CUDA Toolkit 13.4.2，三套运行验收均通过。 +- 当前根目录不是 Git 仓库；版本化与审计需使用明确的文件哈希和状态档案，或后续经用户授权建立版本控制。 + +## 6. 文献初筛结论（尚待矩阵固化） + +- 已存在使用 Sentence-BERT/嵌入进行心理问卷条目匹配和相关结构预测的工作。 +- 已存在用 BERT 嵌入预测 IRT 题目参数、减少试测成本的工作。 +- 已存在把题目语义与心理问卷回答预测结合的 proof-of-concept。 +- 多组 IRT alignment、跨国 DIF、非相同题集/回答格式的测量协调及复杂抽样下的 Bayesian pseudo-posterior 均有直接相关文献。 +- 因而项目贡献不能仅表述为“首次使用 LLM 做心理测量”。候选贡献应聚焦在：官方多调查题库、跨国/跨工具连接、语义条件化的题目参数与 DIF、不确定性传播、严格外层迁移验证及复杂抽样一致性；该定位仍需系统核验。 + +## 7. 未解决问题与风险 + +| 优先级 | 问题 | 影响 | 下一处理 | +|---|---|---|---| +| P0 | 官方题目文本、回答标签、适用总体和跳题规则尚未逐项核验 | 阶段 1 和确认性分析不能启动 | 在阶段 1 建立来源可追踪题库并保留 provisional 状态 | +| P0 | 题目共现、锚定图与可识别性尚未重算 | 不能确定 IRT/层级模型是否成立 | 完成阶段 1 后进入阶段 2 审计 | +| P1 | 数据来源许可及可分发范围尚未逐来源完成最终复核 | 影响复现包与衍生数据交付 | 已在清单中显式标为 review required；阶段 1 按实际纳入来源逐项确认，不默认允许再分发 | +| P1 | R、PyMC/ArviZ、心理测量包与 CUDA Toolkit 曾缺失 | 已解决：项目 Python/R 环境和 CUDA 端到端验收通过 | 后续仅按锁定文件维护；任何升级必须重跑三套烟雾测试 | +| P1 | 文献初筛尚未形成可审计矩阵 | 研究定位和“贡献”表述仍不稳定 | 建立 literature matrix，记录 DOI、来源、证据层级和项目关系 | +| P2 | 根目录没有 Git 版本控制 | 变更追踪依赖手工哈希和档案 | 暂不擅自初始化；先维持文件哈希和行动日志 | + +## 8. 行动日志 + +### A-20260920-001：读取工作流并确定执行入口 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：定位并完整读取研究工作流，核对项目定位文件与模型说明。 +- 输入：`Document/research-workflow.md`、`Document/项目的正式定位.md`、`Document/language-conditioned-psychometric-harmonization-model.md`。 +- 输出：确定从阶段 0 开始，不直接进入模型训练。 +- 检查：工作流明确第一批需完成阶段 0–2；默认 academic-pipeline 的“已有数据→写作”路由不适用于本项目。 +- 未解决问题：阶段 0 四项交付物尚未完成。 +- 下一步：盘点数据与环境，建立阶段 0 文件。 + +### A-20260920-002：本地数据与说明文件初步盘点 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：读取 GSHS、YRBS、NSDUH/YRBS 整理包、WHO 国家背景数据及整理说明；统计文件数量和总容量。 +- 输入：各数据目录 README、变量注意事项和既有质量报告。 +- 输出：确认 734 个文件、约 20.47 GB；识别不同数据源的分析就绪状态差异。 +- 检查：未把 README/质量报告中的数字冒充本轮重算结果。 +- 未解决问题：许可、官方文本、2021/2023 YRBS 布局及设计变量仍需验证。 +- 下一步：生成全量哈希清单。 + +### A-20260920-003：生成数据文件哈希清单 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：对 `Dataset` 下全部 734 个文件计算 SHA-256、记录大小、修改时间、扩展名并标注重复哈希组。 +- 输入：`Dataset/**` 原始及衍生文件；未修改原文件。 +- 输出：`research/audit/data_manifest.csv`（734 行）。 +- 检查：清单已生成且可读取；重复哈希分组已写入。重复组的汇总统计仍需在正式审计报告中重新计算并说明口径。 +- 未解决问题：需补充 dataset group、来源、许可和分析就绪字段。 +- 下一步：扩充清单元数据并抽样复核哈希。 + +### A-20260920-004：最接近方法文献第一轮初筛 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：检索 NLP 问卷协调、文本预测 IRT 参数、多组测量不变性/DIF 和复杂抽样 Bayesian 推断相关工作。 +- 输入：同行评审论文页面、PubMed/PMC、ACL Anthology 及预印本页面。 +- 输出：形成第 6 节的初步定位结论。 +- 检查：明确区分同行评审论文与预印本；未把搜索摘要直接当作完整证据综合。 +- 未解决问题：尚需逐条核验作者、年份、DOI、研究设计、限制和与本项目的差异。 +- 下一步：建立 `literature_matrix.csv`。 + +### A-20260920-005：建立持续进度档案和项目记忆规则 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：按用户要求创建本档案，并在 `AGENTS.md` 固化每次行动前后读取/更新规则。 +- 输入：`Document/research-workflow.md`、当前项目状态及已生成产物。 +- 输出：`Progression.md`、`AGENTS.md`。 +- 检查：阶段状态、交付物、风险、已执行动作和下一步已与工作流对齐。 +- 未解决问题：无。 +- 下一步：重新读取工作流与本档案后，继续完成阶段 0 四项交付物。 + +### A-20260920-006：完成运行环境审计 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：核验操作系统、CPU、RAM、磁盘、GPU/CUDA、核心工具和 Python 科学计算/心理测量/Bayesian 包。 +- 输入：本机只读系统接口、Python distribution metadata、`nvidia-smi`、PyTorch CUDA 最小检查；工作流 SHA-256 为 `394a191359298132fc2a56304dbc49b5934f47af22d2e0fff47d28a0d9edcaeb`。 +- 输出：`research/audit/environment_audit.md`；`.learnings/ERRORS.md` 新增两条已解决的审计方法错误。 +- 检查：确认 31.82 GiB RAM、E 盘可用 256.17 GiB、RTX 5060 Ti 16 GB，且 PyTorch CUDA 可用；直接从 distribution metadata 核验包版本。 +- 未解决问题：R/Rscript、PyMC/PyTensor/ArviZ、SEM/IRT/复杂抽样栈缺失；具体嵌入模型权重和许可未冻结。 +- 后续状态：上述软件缺口已在 A-20260920-010 至 A-20260920-035 中解决；嵌入模型权重与许可仍留待阶段 5 前冻结。 +- 下一步：补齐数据清单的来源、许可和分析就绪字段，并生成重复文件摘要。 + +### A-20260920-007：完成数据来源登记与重复摘要 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：为 734 个文件补充数据组、权威来源、项目角色、候选分析入口、分析就绪状态及许可/分发状态；按 SHA-256 汇总重复文件。 +- 输入：`research/audit/data_manifest.csv` 原始哈希清单；GSHS、YRBS、NSDUH/YRBS 整理包与 WHO 国家背景数据的本地来源说明。 +- 输出：更新后的 `research/audit/data_manifest.csv`；新增 `data_source_registry.csv`、`dataset_group_summary.csv`、`duplicate_summary.csv`。 +- 检查：734 行全部匹配来源登记，未匹配数为 0；117 个重复组包含 283 个文件与 166 个额外副本，潜在冗余约 0.054 GiB。更新后 manifest SHA-256 为 `0427a19c3287a2691edbd9a4486917989fe369fcb0f729a01477ce27cd8f98f2`。 +- 未解决问题：许可状态是审计路由，不是法律授权；GSHS、NSDUH、YRBS 及外部候选在实际纳入和分发前仍须核对现行来源条款。重复文件因 provenance 和脚本引用暂不删除。 +- 下一步：完成 `research/protocol/project_charter.md`，冻结 Gate 0 的总体、构念、迁移任务、范围外事项和待验证清单。 + +### A-20260920-008：冻结阶段 0 项目章程 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：把项目定位转化为可证伪的主要研究问题，并冻结阶段 0–2 的目标总体政策、构念角色、数据角色、六类迁移任务、估计目标、识别原则、范围外事项、伦理与停止条件。 +- 输入：项目工作流、两份定位文件、数据来源登记和环境审计；输入 SHA-256 已写入章程。 +- 输出：`research/protocol/project_charter.md` v0.1.0。 +- 检查：未预设所有题目可做 IRT，未预设语义模型成功，未把学校样本外推到全部青少年；改善阈值和非劣界限留待阶段 2–3 依据审计与模拟冻结。 +- 未解决问题：共同年龄支持域、实际构念模型、主要指标数值、许可细节和正式软件环境仍按章程待验证。 +- 下一步：完成并逐条核验 `research/protocol/literature_matrix.csv`，明确最接近工作与项目贡献边界。 + +### A-20260920-009：完成聚焦文献矩阵与 Gate 0 验收 + +- 时间：2026-09-20 +- 阶段：0 +- 动作：核验预统计协调、NLP 问卷匹配、文本预测 IRT 参数、多组 alignment/DIF 和复杂抽样 Bayesian 推断的最近方法；建立逐条证据矩阵并执行 Gate 0 检查。 +- 输入：期刊/出版物原页、PubMed/PMC、ACL Anthology 和 arXiv；Deep Research 来源核验规则；Spreadsheets 结构与可视化验证规则。 +- 输出：`research/protocol/literature_matrix.csv`、`literature_matrix.xlsx`、`literature_positioning.md`。 +- 检查：13 个唯一来源 ID、必填字段缺失 0、HTTPS 来源 13/13；11 条同行评审、2 条预印本；工作簿公式错误扫描为 0，预览无明显截断或结构错误。CSV SHA-256 为 `9a43d729dbb4bf3819f71f1f373513962e4a6dc064927fc428f8f11b4e4a2a7a`。 +- 最终核对：阶段 0 四项交付物与 `Progression.md` 均存在且非空；项目章程 SHA-256 为 `adb85eea055415050b2091d8f2e5b3d7fd63c019a233430a4a55a344cbef6d0d`。 +- 结论：NLP 问卷匹配和 text-to-IRT 已有直接先例；候选贡献必须建立在官方多调查题库、严格外层迁移、语义条件化 DIF/题目参数、不确定性传播及复杂抽样一致性的组合上。该结论是聚焦定位，不是完整首创证明。 +- 未解决问题：正式论文前需扩大检索和引文追踪；许可、题目来源、共同年龄域、锚定图和模型可识别性留待阶段 1–2。 +- 下一步：在用户确认后进入阶段 1，先建立调查/组件、题目实例、回答字典、设计和来源审核的空模式及数据入口审计。 + +### A-20260920-010：核对研究环境兼容性并盘点安装入口 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：按用户授权启动研究环境补齐；重新读取工作流与进度档案，核对 NVIDIA、PyMC、R 与 PyTorch 官方安装信息，并盘点本机包管理器、Python 解释器和现有环境。 +- 输入：NVIDIA CUDA Toolkit 官方下载/归档页、PyMC 官方安装文档、R for Windows 官方页面、PyTorch 官方安装页；本机只读命令检查。 +- 输出：确定采用“系统工具 + 项目独立 Python 环境 + 项目 R 包库”的分层方案；CUDA Toolkit 目标为当前稳定版 13.4.2，PyMC 采用官方推荐的 `pymc[nutpie]`，NumPyro 作为可选后端。 +- 安装布局确认：本机 `uv` 支持通过 `--install-dir`、`--no-registry` 与项目内缓存把 Python 3.12 完整落在 `E:\Model`，并可用指定解释器创建隔离虚拟环境，无需改写系统默认 Python。 +- 检查：本机仅发现 Python 3.13；`winget.exe` 入口存在但当前执行返回“系统无法访问此文件”；R/Rscript、nvcc、conda/mamba 未发现；`uv` 可用；项目根目录不是 Git 仓库。 +- 未解决问题：需改用可访问的官方安装器或修复后的包管理入口安装 R 与 CUDA Toolkit；Python 独立环境尚未创建。 +- 下一步：通过官方安装源部署 Python 研究环境，并分别部署/验收 R、CUDA Toolkit；每批完成后立即回写本档案和环境审计。 + +### A-20260920-011：安装项目内 Python 3.12 解释器 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案后，使用 `uv` 将 CPython 下载到项目内，不注册系统解释器、不替换系统 Python。 +- 输入：官方可分发 CPython 3.12 构建；目标目录 `E:\Model\.python`，缓存目录 `E:\Model\.uv-cache`。 +- 输出：已安装 `E:\Model\.python\cpython-3.12.14-windows-x86_64-none\python.exe`。 +- 检查：下载与解释器安装成功；虚拟环境尚未建立。 +- 异常：首次创建虚拟环境时预写了 `3.12.11` 路径，但实际解析并安装的是 `3.12.14`，因此该子步骤失败；已定位真实解释器路径并写入错误日志。 +- 下一步：以实际解释器路径创建 `.venv-research`，安装并验证研究包。 + +### A-20260920-012：冻结 Python 研究栈安装清单 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，建立环境归档目录，并按工作流需要拆分数据/统计、Bayesian、心理测量、可视化和 NLP/GPU 依赖。 +- 输入：阶段 0 环境审计、PyMC 与 PyTorch 官方安装建议、SEM/心理测量技能依赖要求。 +- 输出：`research/environment/python-requirements.in`。 +- 检查：清单包含此前审计缺失的 `polars`、`duckdb`、`statsmodels`、`pymc`、`pytensor`（由 PyMC 解析）、`arviz`（由 PyMC 解析）、`semopy`、`factor-analyzer`、`pingouin`、`girth`、JAX/NumPyro 后端，以及项目后续 NLP/GPU 基线工具。 +- 未解决问题：尚待依赖解析、安装和导入/数值烟雾测试；精确版本将在安装后导出锁定。 +- 下一步：创建 `.venv-research` 并安装清单，先验收统计/Bayesian/心理测量，再验收 GPU/NLP。 + +### A-20260920-013：创建 Python 3.12 研究环境并安装依赖 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，以项目内 CPython 3.12.14 创建 `.venv-research`，解析并安装冻结输入清单。 +- 输入：`research/environment/python-requirements.in`；PyPI 与包发布者提供的 Windows wheels/sdists。 +- 输出：`.venv-research`；共安装 195 个包，包括 PyMC 6.3.2、PyTensor 3.3.2、ArviZ 1.3.0、NumPyro 0.22.0、semopy 2.3.11、factor-analyzer 0.5.1、pingouin 0.6.1、girth 0.8.0、PyTorch 2.14.0、polars 1.44.2、duckdb 1.5.5、statsmodels 0.15.0。 +- 检查：依赖解析与安装命令返回成功；`semopy` 与 `factor-analyzer` 本地 wheel 构建成功。 +- 未解决问题：安装成功不等于运行有效；需检查依赖一致性、PyMC 最小采样、SEM 拟合、IRT 导入、JAX 后端与 PyTorch GPU。特别需要核验 Torch/TorchVision/TorchAudio 的运行时兼容性。 +- 下一步：运行分层烟雾测试，修复任何兼容错误后再导出精确锁定文件。 + +### A-20260920-014：建立可重复的 Python 环境烟雾测试 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，建立单一测试入口，覆盖核心模块导入、PyMC 最小采样、semopy CFA、factor-analyzer 与 PyTorch CUDA 矩阵运算。 +- 输入：`.venv-research` 当前安装状态。 +- 输出：`research/environment/smoke_test.py`。 +- 检查：测试固定随机种子并以结构化 JSON 报告版本、通过/失败状态和异常回溯；任一必需项失败时返回非零状态。 +- 未解决问题：测试尚未执行。 +- 下一步：先运行依赖一致性检查，再执行烟雾测试并按实际失败修复。 + +### A-20260920-015：执行首轮 Python 环境烟雾测试 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案；运行 `pip check` 和结构化烟雾测试，并归档原始输出。 +- 输入：`.venv-research`、`research/environment/smoke_test.py`。 +- 输出：`research/environment/smoke-test-result.json`（首轮失败记录；包含前置警告，修复后将重写为纯 JSON）。 +- 检查：`pip check` 返回无断裂依赖；数据栈、PyMC/PyTensor/ArviZ/JAX/NumPyro、semopy、factor-analyzer、pingouin、girth、Torch/NLP 包均可导入（TabPFN 除外）；semopy CFA 数值拟合通过。 +- 失败事实：PyMC/nutpie 因默认 PyTensor 缓存写入受限用户目录而失败；TabPFN 因 `skrub_data` 默认目录权限失败；factor-analyzer 0.5.1 调用已从 scikit-learn 1.9 移除的 `force_all_finite` 参数；PyTorch 为 `2.14.0+cpu`，CUDA 不可用；Matplotlib 用户缓存不可写。 +- 结论：当前环境尚未通过验收，不能标记为可用于正式分析。 +- 下一步：把所有运行缓存重定向到项目内；为 factor-analyzer 固定兼容的 scikit-learn；从 PyTorch 官方 CUDA 索引重装版本一致的 Torch 三件套，然后重跑完整测试。 + +### A-20260920-016：修订 Python 运行策略与依赖约束 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，定位 Skrub 的正式缓存环境变量并修订依赖输入与烟雾测试。 +- 输入：首轮烟雾测试回溯、已安装包源码中的缓存配置、PyTorch 官方 CUDA wheel 说明。 +- 输出：更新 `python-requirements.in`（固定 scikit-learn 1.6.1 和匹配的 Torch 三件套）；更新 `smoke_test.py`；新增 `activate-research.ps1`。 +- 检查：PyTensor、Numba、Matplotlib、Skrub、Hugging Face 与 Torch 缓存均统一重定向至 `E:\Model\.cache`；测试脚本改为直接写纯 JSON，避免标准错误警告污染结果文件。 +- 未解决问题：尚需实际降级 scikit-learn、从官方 CUDA 索引重装 Torch 三件套并复测。 +- 下一步：执行版本修复与 GPU wheel 重装。 + +### A-20260920-017：修复 factor-analyzer 兼容性并核对 CUDA wheel 版本 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，将 scikit-learn 从 1.9.1 固定为 1.6.1；尝试从 PyTorch CUDA 13.0 官方索引重装 Torch 三件套，并在解析失败后直接核对官方 wheel 索引。 +- 输入：`python-requirements.in`、PyTorch 官方 cu130 的 torch/torchvision/torchaudio 索引。 +- 输出：scikit-learn 1.6.1 已安装；确认 Python 3.12/Windows 的 `torch 2.14.0+cu130` 与 `torchvision 0.29.0+cu130` 存在。 +- 异常与决定：官方 cu130 索引没有同代 `torchaudio 2.14.0`，最高列出的相关版本滞后；本项目不处理音频，故移除非必要 torchaudio，避免制造运行时不一致。 +- 检查：失败发生在依赖解析阶段，原 Torch 安装未被改写；requirements 与烟雾测试已同步移除 torchaudio。 +- 下一步：只从官方 cu130 索引重装匹配的 torch 与 torchvision，卸载旧 torchaudio，并重跑验收。 + +### A-20260920-018：安装匹配的 PyTorch CUDA 运行栈 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，卸载不匹配且非必要的 torchaudio，从 PyTorch 官方 CUDA 13.0 索引重装 Torch 2.14.0 与 TorchVision 0.29.0。 +- 输入：PyTorch 官方 `cu130` wheel 索引；Python 3.12/Windows x86_64。 +- 输出：`torch 2.14.0+cu130`、`torchvision 0.29.0+cu130`；torchaudio 已移除。 +- 检查：官方 CUDA wheel 下载约 1.9 GB；替换过程成功完成，没有改动其余研究包的版本约束。 +- 未解决问题：尚需以 `torch.cuda.is_available()`、GPU 名称和实际矩阵运算验收；完整烟雾测试还需确认缓存修复和 factor-analyzer 修复。 +- 下一步：运行第二轮完整 Python 烟雾测试与依赖检查。 + +### A-20260920-019：Python 研究环境通过第二轮运行验收 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案；运行第二轮 `pip check` 与完整烟雾测试。 +- 输入：修复后的 `.venv-research`、项目内缓存策略和 `smoke_test.py`。 +- 输出：纯 JSON 结果 `research/environment/smoke-test-result.json`。 +- 检查：依赖检查无断裂；所有 25 个目标模块导入通过；PyMC 6.3.2 使用 nutpie 完成 1 链 50 tune + 50 draws 的最小采样；semopy CFA 与 factor-analyzer 拟合通过；TabPFN 9.0.0 导入通过；Torch 2.14.0+cu130 在 NVIDIA GeForce RTX 5060 Ti 上完成有限值矩阵乘法，`torch.cuda.is_available()` 为真。 +- 限制：烟雾测试的 50 draws 只用于验证运行能力，不构成统计诊断；`g++ not available` 是可选编译器探测提示，nutpie 实际采样已成功；factor-analyzer 使用 scikit-learn 1.6 的弃用兼容层，故必须保持版本锁定。 +- 结论：Python 统计/Bayesian/心理测量/GPU 环境通过当前阶段运行验收。 +- 下一步：导出精确 Python 锁定清单；安装并验收 R/CRAN 栈与 CUDA Toolkit/nvcc。 + +### A-20260920-020：安装并验证项目内 R 4.6.1 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，从 CRAN 官方站点下载 R 4.6.1 Windows x64 安装器，并静默安装到项目工具目录。 +- 输入：`https://cran.r-project.org/bin/windows/base/R-4.6.1-win.exe`。 +- 输出：`E:\Model\.tools\R-4.6.1`；安装器 SHA-256 为 `C5424C40CD70EF85765A55D2FF96BB602B5F30ED536938FF004F14DB5DB3C2DF`。 +- 检查：`Rscript (R) version 4.6.1 (2026-06-24)`、UCRT、x64；基础启动成功。 +- 异常：静默安装器首次返回时立即调用 Rscript 命中短暂落盘竞态，随后只读检查确认安装完整并复测通过；另发现当前 shell 的 `C.UTF-8` locale 设置在 Windows R 中产生警告，需在项目启动脚本中清理/修正。 +- 未解决问题：CRAN 研究包尚未安装；项目 R 库与运行测试尚未建立。 +- 下一步：创建项目专属 R library，安装 survey/srvyr、mirt、lavaan/semTools/psych、数据处理与复现包并执行烟雾测试。 + +### A-20260920-021：冻结 R 包清单与运行验收协议 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，识别 Windows R locale 覆盖来源，建立项目 R 包安装脚本和数值烟雾测试。 +- 输入：工作流对复杂抽样、IRT/SEM、数据处理和复现性的要求；当前 shell 的 `LANG/LC_ALL/LC_CTYPE=C.UTF-8`。 +- 输出：`install-r-packages.R`、`r-smoke-test.R`；更新 `activate-research.ps1`，加入 R_HOME、R_LIBS_USER、PATH 与 Windows locale 清理。 +- 检查：包契约包括 survey/srvyr、mirt、lavaan/semTools/psych、Arrow/Haven/tidyverse 核心、renv/targets；测试覆盖加权均值、lavaan CFA、mirt 2PL 与 Parquet 往返。 +- 可复现性修订：两个 R 脚本均从 `Rscript` 的 `--file=` 参数解析自身路径，不依赖当前工作目录或交互式调用栈。 +- 未解决问题：CRAN 包尚未实际安装和运行。 +- 下一步：安装项目 R library，导出版本并运行完整烟雾测试。 + +### A-20260920-022：安装并核对项目 R 研究包 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，从 CRAN 官方镜像安装项目 R 包及必需依赖到 `.r-library/4.6`。 +- 输入：`install-r-packages.R`；CRAN Windows 4.6 二进制仓库。 +- 输出：项目 R library；`research/environment/r-package-versions.json`。 +- 检查：19 个顶层包全部安装并经 `packageVersion()` 核对，包括 survey 4.5、srvyr 1.3.1、mirt 1.47、lavaan 0.7-2、semTools 0.5-9、psych 2.6.5、arrow 25.0.1、renv 1.2.4、targets 1.12.0；安装命令返回成功。 +- 未解决问题：包存在性已验证，但加权估计、CFA、2PL 和 Parquet 读写尚待运行测试。 +- 下一步：执行 `r-smoke-test.R` 并根据实际运行结果修复。 + +### A-20260920-023：执行首轮 R 运行验收并发现判定漏洞 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，运行 R 包导入、复杂抽样加权均值、lavaan CFA、mirt 2PL 和 Arrow/Parquet 往返测试。 +- 输入：项目 R 4.6.1 与 `.r-library/4.6`。 +- 输出：`research/environment/r-smoke-test-result.json`（首轮结果，保留审计痕迹，下一轮将覆盖为最终结果）。 +- 检查：19/19 顶层包导入通过；survey 加权均值、lavaan CFA 和 Arrow 往返通过；无 locale 警告。 +- 失败事实：mirt 在独立随机 Bernoulli 数据上 500 次 EM 后未收敛；更重要的是，测试脚本仅记录 `converged=false` 却仍把该项标为 passed，属于验收逻辑错误。 +- 修复：改用已知一维 2PL 生成机制模拟 500×8 响应矩阵；把未收敛显式提升为测试失败；最大 EM 周期设为 1000。 +- 结论：R 环境尚不能在修复后复测前标记为最终通过。 +- 下一步：重跑 R 烟雾测试，必须同时满足过程成功和 `mirt@OptimInfo$converged=true`。 + +### A-20260920-024：R 研究环境通过第二轮运行验收 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，执行修复后的完整 R 烟雾测试。 +- 输入：R 4.6.1、项目 library、已知一维 2PL 模拟数据生成器。 +- 输出：最终 `research/environment/r-smoke-test-result.json`。 +- 检查：19/19 顶层包导入通过；survey 加权均值为有限值；lavaan CFA 明确收敛；mirt 2PL 在 500 名模拟受访者、8 个题目上明确收敛；Arrow Parquet 写入后读回 3×2 数据；命令返回成功且无 locale 警告。 +- 结论：R 复杂抽样、IRT/SEM 和列式数据交换环境通过当前阶段运行验收。 +- 下一步：安装 CUDA Toolkit/nvcc；随后导出 Python/R 锁定清单并更新环境审计。 + +### A-20260920-025：解析并冻结 CUDA Toolkit 官方安装源 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，从 NVIDIA 官方动态下载页解析 Windows x86_64 的 CUDA 13.4.2 安装器 URL、大小和校验和，并核对 Windows 安装指南。 +- 输入：NVIDIA CUDA Toolkit 13.4.2 下载页与 Microsoft Windows 安装指南。 +- 输出：网络安装器 `cuda_13.4.2_windows_x86_64_network.exe`；官方 URL 固定为 `https://developer.download.nvidia.com/compute/cuda/13.4.2/network_installers/cuda_13.4.2_windows_x86_64_network.exe`；大小 10,460,392 bytes；MD5 `a0810494c821437671d6e68e66f59daf`。 +- 检查：官方文档确认 CUDA 13.1 起 Windows Toolkit 不再捆绑 GPU 驱动，故安装 Toolkit 不应替换现有驱动。 +- 未解决问题：安装器尚未下载/校验/执行；nvcc 尚未验收。 +- 下一步：下载并核对 MD5，静默安装 Toolkit，验证 nvcc 版本、目录和 PyTorch GPU 回归。 + +### A-20260920-026：安装 CUDA Toolkit 13.4.2 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，下载 NVIDIA CUDA 13.4.2 Windows x86_64 网络安装器，校验后执行完整静默安装；安装期间通过进程 CPU 和组件展开目录做只读健康检查。 +- 输入：A-20260920-025 冻结的 NVIDIA 官方安装器。 +- 输出：系统级 CUDA Toolkit 13.4.2；安装器保存在 `.downloads/cuda_13.4.2_windows_x86_64_network.exe`。 +- 检查：下载文件 MD5 与官方值 `a0810494c821437671d6e68e66f59daf` 完全一致；安装器最终退出码为 0；期间观察到 cudart、cuSPARSE、cuSOLVER、NPP 等 13.4 组件展开，未启动第二安装实例。 +- 未解决问题：需独立验证最终 nvcc、环境变量和 PyTorch GPU 回归；Windows C++ host compiler 状态尚未检查。 +- 下一步：执行 Toolkit 与 GPU 回归验收，并判断是否需要补充 MSVC Build Tools。 + +### A-20260920-027：完成 CUDA Toolkit 基础与 GPU 回归验收 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，独立核验 Toolkit 目录、nvcc、NVIDIA 驱动、PyTorch GPU 运算和 Windows C++ host compiler。 +- 输入：系统级 CUDA 13.4.2、现有 NVIDIA 驱动、项目 Python GPU 环境。 +- 输出：CUDA 根目录 `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4`。 +- 检查：`nvcc` 报告 release 13.4 / V13.4.92；GPU 为 NVIDIA GeForce RTX 5060 Ti、驱动 596.36、显存 16,311 MiB；PyTorch 2.14.0+cu130 使用 CUDA runtime 13.0 完成 GPU 矩阵运算且结果有限；Visual Studio 2022 Community C++ 工具链已发现，无需另装 Build Tools。 +- 未解决问题：尚需用 nvcc 和 MSVC 编译并运行最小 CUDA 程序，完成端到端 Toolkit 验收。 +- 验收准备：新增 `research/environment/cuda-smoke.cu`，以单线程 kernel 将 41 增为 42；项目启动脚本已加入 CUDA_PATH 与 Toolkit `bin`。 +- 下一步：编译/运行 CUDA smoke test；随后锁定环境与更新审计。 + +### A-20260920-028：确认 GPU 架构并修正 CUDA 编译调用 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，查询 GPU compute capability，并首次尝试以 Visual Studio 开发环境调用 nvcc。 +- 输入：`cuda-smoke.cu`、Visual Studio 2022 Community、CUDA 13.4 nvcc。 +- 输出：确认 RTX 5060 Ti compute capability 为 `(12, 0)`，编译目标应为 `sm_120`。 +- 异常：嵌套 `cmd.exe` 命令对带空格的 `VsDevCmd.bat` 路径引号处理失败，编译器未实际启动，未生成或执行二进制。 +- 修复方案：先从 `VsDevCmd.bat && set` 捕获开发环境变量并导入当前 PowerShell 进程，再直接调用 nvcc，避免多层命令字符串。 +- 下一步：按修复方案重新编译并运行 smoke test。 + +### A-20260920-029：精确核验 MSVC 并统一 CUDA 构建 shell + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案；第二次编译已到达 nvcc，但导入 PowerShell 的 VS 环境未让 nvcc 找到 `cl.exe`；随后用 vswhere 精确核验 C++ 组件与编译器文件。 +- 输入：Visual Studio Installer `vswhere.exe`、CUDA smoke 源码。 +- 输出：确认 MSVC `cl.exe` 位于 `VC/Tools/MSVC/14.44.35207/bin/Hostx64/x64/cl.exe`；新增 `cuda-smoke-build.cmd`。 +- 异常：跨进程导入 VsDevCmd 环境未满足 nvcc 的 host compiler 查找，报 `Cannot find compiler 'cl.exe' in PATH`。 +- 修复：让 `vcvars64.bat`、nvcc 和测试程序在同一个 cmd 进程内顺序运行，避免跨 shell 环境复制。 +- 下一步：运行统一构建脚本并验收结果 42。 + +### A-20260920-030：CUDA Toolkit 端到端验收通过 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，在同一 cmd 环境中加载 MSVC、以 nvcc `-arch=sm_120` 编译最小 CUDA kernel，并在 GPU 上运行。 +- 输入：`cuda-smoke.cu`、`cuda-smoke-build.cmd`、MSVC 14.44、CUDA 13.4.92、RTX 5060 Ti。 +- 输出：`research/environment/cuda-smoke.exe`（可重建测试产物）。 +- 检查：编译和链接成功，运行输出 `cuda_smoke_result=42`，进程退出码 0；证明 nvcc、MSVC host compiler、CUDA runtime 与 GPU 的完整链路可用。 +- 警告处理：MSVC 在中文代码页下对 NVIDIA 头文件产生 C4819 编码警告，不影响构建或运行；构建脚本已加 `/wd4819` 仅屏蔽该第三方头文件警告。 +- 结论：CUDA Toolkit 13.4.2 通过当前阶段验收。 +- 下一步：导出 Python/R 精确依赖清单、环境哈希和使用说明，更新 `environment_audit.md` 与本档案的风险状态。 + +### A-20260920-031：首次环境锁定失败并修正缓存路径 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，尝试导出 Python freeze、renv.lock、R 全包表与 session info。 +- 输入：已通过验收的 Python/R 环境。 +- 失败事实：`uv` 默认缓存 `C:\Users\Jino\AppData\Local\uv\cache` 和 renv 默认缓存 `C:\Users\Jino\AppData\Local/R/cache/R/renv` 均不可写；锁文件未生成。 +- 修复：启动脚本新增 `UV_CACHE_DIR=E:\Model\.uv-cache` 与 `RENV_PATHS_ROOT=E:\Model\.cache\renv`；下一次锁定命令也将显式设置两者。 +- 下一步：重跑全部锁定导出，并只在四个目标文件均存在后计算哈希。 + +### A-20260920-032：第二次锁定生成核心文件但全包表导出失败 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，使用项目缓存重跑 Python freeze 和 renv snapshot。 +- 输出：已生成 `python-requirements.lock.txt` 与 `renv.lock`；renv 记录 R 4.6.1 和项目 CRAN 包。 +- 异常：R 全包表固定索引 `Repository` 列，但本机 `installed.packages()` 结果没有该列，触发 `subscript out of bounds`；同时 renv 只收到项目库路径，因而把 R 自带推荐包（MASS、Matrix、survey 依赖等）误报为缺失。 +- 修复方案：向 renv 同时传入项目 library 与 `.Library`，全包 CSV 仅选择实际存在的列；重新生成所有 R 锁定辅助文件并统一核验。 +- 结论：已有锁文件视为中间产物，尚未完成最终验收。 +- 下一步：按修复方案重跑 R snapshot/导出并计算全部哈希。 + +### A-20260920-033：完成 Python/R 环境锁定与哈希核验 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，以项目缓存和完整 R library 搜索路径重跑锁定导出；逐文件检查存在性并计算 SHA-256。 +- 输出：`python-requirements.lock.txt`（195 包）、`renv.lock`（119 包）、`r-installed-packages.csv`（134 行）、`r-session-info.txt`，以及现有 Python/R 测试 JSON。 +- 检查：七个要求产物均存在且非空；哈希已取得，将写入正式环境审计。 +- 异常：一次 PowerShell 表格序列化只显示占位列，随后改用纯文本逐行输出，成功取得完整哈希；文件本身未受影响。 +- 下一步：更新环境审计、使用说明与进度风险状态。 + +### A-20260920-034：正式环境审计替换补丁被工具拒绝 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：尝试在单一补丁中删除并重新新增 `environment_audit.md`，同时创建 README 和更新进度状态。 +- 结果：补丁工具拒绝同一文件的多重操作；未修改任何目标文件。 +- 修复方案：将审计文件替换拆成独立删除与新增步骤，再单独添加 README 和进度变更。 +- 下一步：按拆分方案完成文档收尾并复核。 + +### A-20260920-035：完成环境补强文档收口与最终一致性复核 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：重新读取工作流与进度档案，更新正式环境审计与使用说明，复核锁文件、测试 JSON、依赖一致性和 CUDA 可重建性，并清理当前状态中的旧阻断措辞。 +- 输出：更新 `research/audit/environment_audit.md`；新增 `research/environment/README.md`；更新本档案至 v1.2。 +- 检查：8 个要求产物均存在且非空；Python 测试失败数 0，R 测试失败数 0，mirt `converged=true`；`pip check` 无断裂依赖；CUDA 重新编译运行输出 42；环境审计记录七项 SHA-256。 +- 工具异常：README/进度联合补丁因补丁区块顺序与文件行序不一致而被整体拒绝一次；拆分并按行序重排后成功，未丢失内容。 +- 结论：用户要求的 R、CUDA Toolkit 与缺失 Python/R 研究包已安装、验证、锁定并归档。阶段 0 环境风险解除。 +- 未解决问题：具体嵌入模型权重和许可尚未冻结；这是阶段 5 前的研究治理任务，不是环境安装缺陷。 +- 下一步：进入阶段 1，建立有来源证据的题库与回答层。 + +### A-20260920-036：最终交付完整性确认 + +- 时间：2026-09-20 +- 阶段：0（环境补强） +- 动作：按强制规则重新读取工作流与进度档案，逐项检查环境审计、使用说明、输入/锁定文件、Python/R 测试结果和 CUDA 构建脚本。 +- 检查：8 个核心交付文件全部存在且非空；进度版本为 1.2，当前阶段与下一行动表述一致；正式环境审计明确标记 `passed` 并记录 CPython 3.12.14、R 4.6.1、CUDA Toolkit 13.4.2 和 CUDA 结果 42。 +- 结论：本次工具补强请求完成，无待安装的工作流关键环境依赖。 +- 下一步：阶段 1 题库与回答层。 + +### A-20260920-037：审计 Git 初始化条件 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：按强制规则读取工作流、进度档案与 `AGENTS.md`，核对项目根目录、Git 用户身份和远程配置。 +- 输入：`E:\Model` 根目录、全局 Git 配置。 +- 检查：当前尚无 Git 仓库；全局提交身份已配置为 `Jinotech `；未发现可用远程地址。 +- 决定：以 `main` 初始化本地仓库；排除 Dataset、解释器、虚拟环境、R library、安装器和缓存，纳入研究代码、协议、审计、锁文件与进度档案。 +- 推送状态：`push_pending`；原因是尚无远程 URL，不影响本地初始化和首个提交。 +- 下一步：建立 `.gitignore`，把逐行动提交/推送协议写入 `AGENTS.md`，初始化并审计暂存范围。 + +### A-20260920-038：核对首个 Git 提交的目录规模 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流与进度档案，递归统计顶层目录文件数与容量。 +- 检查：`Dataset` 约 19.06 GiB；`.uv-cache` 约 4.76 GiB；`.venv-research` 约 4.39 GiB；`.r-library` 约 0.23 GiB；`.tools` 约 0.19 GiB；`.downloads` 约 0.10 GiB，均不应进入 Git。`LLM-based Agent构建` 约 0.33 GiB，需检查其大文件后再决定细粒度忽略。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：检查 `LLM-based Agent构建` 最大文件，随后冻结 `.gitignore`。 + +### A-20260920-039：审计本地 LLM 文献归档的大文件 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流与进度档案，检查 `LLM-based Agent构建` 的最大文件。 +- 检查：该目录约 0.33 GiB，主要为 72 个本地 PDF 文献副本；单文件最大约 26.70 MiB。当前心理测量项目已有可审计的文献矩阵与来源链接，不需要把这批相邻主题 PDF 二进制纳入首个仓库。 +- 决定：将整个 `LLM-based Agent构建/` 作为本地参考归档排除，避免无必要地膨胀远程历史；不删除原文件。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：创建 `.gitignore` 并更新 `AGENTS.md` 的 Git 提交/推送协议。 + +### A-20260920-040：冻结版本化边界与逐行动 Git 协议 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流与进度档案，创建根目录 `.gitignore`，并在 Codex 实际识别的 `AGENTS.md` 中加入强制 Git 协议。 +- 输出：`.gitignore`；更新 `AGENTS.md`。 +- 检查：规则要求每项推进在验证和更新进度后形成独立提交；已配置远程时立即 push；无远程或推送失败时必须记录 `push_pending/push_failed`，不得虚报。原始数据、环境、安装器、缓存、模型权重和可重建二进制明确排除。 +- 说明：用户口述的 `AGENT.md` 对应本项目已存在且被 Codex 识别的 `AGENTS.md`，故更新现有文件而不建立冲突的第二份规则文件。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：初始化 `main` 仓库，检查忽略结果与待提交文件大小，然后创建首个提交。 + +### A-20260920-041：初始化 Git 仓库并遇到安全所有权检查 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流与进度档案，在 `E:\Model` 初始化 `main` 分支仓库并尝试读取状态/忽略规则。 +- 输出：`E:\Model\.git`，空仓库初始化成功。 +- 异常：Git 检测到目录所有者为 Windows Administrators、当前受控账户为 CodexSandboxOffline，因 `dubious ownership` 拒绝状态读取。 +- 修复方案：仅将精确项目路径 `E:/Model` 加入全局 `safe.directory`，不使用通配符或关闭全局安全检查。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：应用精确安全例外并重新审计忽略范围。 + +### A-20260920-042：应用精确 Git 安全例外并验证忽略边界 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流与进度档案，将 `E:/Model` 精确加入全局 `safe.directory`，重新读取仓库状态并逐项检查忽略命中。 +- 检查：Git 状态可正常读取；候选顶层仅有 `.gitignore`、`.learnings/`、`AGENTS.md`、`Document/`、`Progression.md` 和 `research/`。Dataset、Python/R 环境、工具安装目录、下载、缓存、本地 PDF 归档及 CUDA 可执行文件均由预期规则排除。 +- 安全边界：未使用 `safe.directory=*`，只放行当前仓库绝对路径。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：审计候选文件数量、总容量和最大文件，确认后暂存首个提交。 + +### A-20260920-043：发现并排除嵌套 node_modules + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流、进度档案和 Git 状态，统计未跟踪候选文件及最大文件。 +- 检查：初始候选为 7,832 个文件、约 320.92 MiB；最大文件均位于 `research/experiments/node_modules`，属于可重建工具依赖。 +- 修复：`.gitignore` 新增 `**/node_modules/`，保留实验目录中的源文件和配置但排除依赖树。 +- 附带警告：Git 无法读取用户级 `C:\Users\Jino\.config\git\ignore`，不影响项目级 ignore；不为此扩大文件系统权限。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：重新审计候选规模，确认后暂存。 + +### A-20260920-044：确认首个提交候选集安全且轻量 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流、进度档案与 Git 状态，应用新 ignore 后重新统计全部候选路径。 +- 检查：候选集降至 36 个文件、约 1.25 MiB；最大单文件约 0.32 MiB。候选仅包含规则/进度、项目说明、研究协议与审计、环境脚本/锁文件/测试结果、文献矩阵及其生成脚本；没有原始数据、环境目录、node_modules、安装器、模型权重或可执行文件。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：暂存 36 个候选文件，检查 staged diff/stat 后创建首个提交。 + +### A-20260920-045：首次暂存因 `.git` 写权限受限而失败 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流、进度档案与 Git 状态，尝试暂存全部已审计候选并查看 staged diff。 +- 结果：`git add` 无法创建 `.git/index.lock`，没有文件进入暂存区；工作树内容未变。 +- 修复方案：以用户已授权的仓库写操作权限重试；Git 命令显式设置 `core.excludesFile=NUL`，避免不可读用户级 ignore 警告，同时继续使用项目 `.gitignore`。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：提升仅 Git 索引写入所需权限，重新暂存和核对。 + +### A-20260920-046：无效 excludesFile 覆盖导致第二次暂存前退出 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：在获准的 Git 索引写权限下重试暂存，并尝试用 `core.excludesFile=NUL` 消除不可读用户级 ignore 警告。 +- 结果：Git for Windows 报 `cannot use NUL as an exclude file`，在写索引前退出；暂存区仍为空。 +- 修复：移除非必要的 excludesFile 覆盖，接受不影响项目 `.gitignore` 的用户级警告，直接执行已授权的 `git add -A`。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：无覆盖地暂存并核对 staged 范围。 + +### A-20260920-047：暂存并核对首个仓库快照 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：重新读取工作流、进度档案与 Git 状态，在获准的仓库元数据写权限下暂存全部已审计候选，并检查 staged stat 与逐文件状态。 +- 检查：36 个文件全部为新增；约 9,150 行；仅含两个小型二进制研究产物（10 KiB 工作簿、约 328 KiB 预览图）；暂存清单与 A-20260920-044 的边界一致。 +- 警告：Git 提示部分 LF 文件未来可能按 Windows 配置转为 CRLF；当前内容与暂存均成功，不作为阻断。 +- 推送状态：`push_pending`，远程仍未配置。 +- 下一步：把本条记录重新暂存，创建首个提交并验证 HEAD/工作树/远程状态。 + +### A-20260920-048：准备创建首个可复现仓库提交 + +- 时间：2026-09-20 +- 阶段：项目治理 +- 动作：将以非交互式提交 `chore(repo): initialize reproducible research workspace` 固化工作流、项目记忆、阶段 0 产物、环境锁与 Git 治理规则。 +- 完成条件：提交命令成功、HEAD 可解析、提交后工作树无意外未提交文件。 +- 推送状态：`push_pending`；没有远程 URL，无法执行真实 `git push`，不得声称已推送。 +- 下一步：创建并验证提交；待用户提供远程 URL 后设置 `origin` 并推送 `main`。 + +## 9. 下一行动 + +下一次行动开始前重新读取 `Document/research-workflow.md` 与本文件。随后： + +环境补强已经完成。下一行动进入阶段 1： + +1. 冻结阶段 1 数据实体 schema 与审核状态枚举； +2. 只读核验 GSHS、YRBS、NSDUH 候选入口的实际结构、行列数和设计字段； +3. 建立主要结局的来源登记骨架，所有未完成实际双重核查的条目标为 `provisional`。 diff --git a/research/audit/data_manifest.csv b/research/audit/data_manifest.csv new file mode 100644 index 0000000..7686d6f --- /dev/null +++ b/research/audit/data_manifest.csv @@ -0,0 +1,735 @@ +"relative_path","dataset_group","authority","source_url","project_role","canonical_analysis_entry","readiness","license_distribution_status","bytes","last_write_utc","sha256","duplicate_group","extension" +"Dataset\_organized\duplicate_audit.md","organization metadata","local project organization","","project metadata","none","documentation_only","not_applicable","1136","2026-09-14T21:42:47.6617710Z","dbe896cfe78f9fe30635c0d350fb495b690d01110949afd2dd2668da27fefa93","",".md" +"Dataset\_organized\external_candidates\Global_Mind_Project\gmp_data_20200101-20221231.csv","Global Mind external candidate","Sapien Labs / Global Mind Project","","external candidate","none","hold","unknown_review_required","720368137","2026-09-03T15:24:22.3074481Z","61c02bbb05cb90a541c10306602a88043edd0781fd8209223c002a3893131e31","",".csv" +"Dataset\_organized\external_candidates\Global_Mind_Project\gmp_data_20230101-20241231.csv","Global Mind external candidate","Sapien Labs / Global Mind Project","","external candidate","none","hold","unknown_review_required","935136716","2026-09-03T15:24:17.6497186Z","65f7148b685f9eabd45ac83c53e2bc6af6d1955426309d0c4dd2ed21cedaa315","",".csv" +"Dataset\_organized\external_candidates\Global_Mind_Project\README_ Information on the Global Mind data on Brainbase.docx","Global Mind external candidate","Sapien Labs / Global Mind Project","","external candidate","none","hold","unknown_review_required","512023","2026-09-03T15:22:45.9050221Z","bc0a278ec4683e67b7fa41ad378efd6ef502eefa78418e6813baa00a6cdc1045","",".docx" +"Dataset\_organized\README.md","organization metadata","local project organization","","project metadata","none","documentation_only","not_applicable","1153","2026-09-19T13:21:12.5860663Z","7886b255d9a5cba2d042306f8c99e91c702dcafbcd97950497511edd76d34297","",".md" +"Dataset\可直接分析数据包_NSDUH_YRBS.zip","NSDUH/YRBS processed delivery archive","local archive of processed delivery","","provenance archive","none","archive_only","inherits_upstream_terms","138448123","2026-09-05T05:40:04.3487024Z","3d1f27934ef6c09bd0acda0775c317efca4893a251e4b400f59e627eb94dc212","",".zip" +"Dataset\可直接分析数据包_NSDUH_YRBS\harmonization\construct_crosswalk.csv","NSDUH/YRBS processed delivery","SAMHSA NSDUH DAS API and US CDC YRBS programs","https://datatools.samhsa.gov/; https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","processed candidate inputs","Dataset\可直接分析数据包_NSDUH_YRBS\NSDUH\nsduh_2021_2024_full.parquet; Dataset\可直接分析数据包_NSDUH_YRBS\YRBS\full_by_year","conditional","upstream_terms_review_required","523190","2026-09-05T05:32:31.9198945Z","81568508179e393f441cf4b009fb9e1048a866b74e72ae3a616d7da3e53cb60f","",".csv" +"Dataset\可直接分析数据包_NSDUH_YRBS\harmonization\missing_value_rules.csv","NSDUH/YRBS processed delivery","SAMHSA NSDUH DAS API and US CDC YRBS programs","https://datatools.samhsa.gov/; https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","processed 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layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","2410082","2026-09-03T17:03:55.6765729Z","f0a41a9a84562f7ad52cf938c4e21d847549d5b42b5e9649e81c3cfd2015bf89","",".json" +"Dataset\WHO-country_context\README.md","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","3083","2026-09-03T17:04:35.1348531Z","814d5d5e131168bbefb23536c737919c66ff95278688ac1a5d7fdb5b46343354","",".md" +"Dataset\WHO-country_context\who_candidate_indicator_catalog.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","1000","2026-09-03T17:03:57.9100888Z","2bd0c3cf0c0d0eef59f99f4c0d4f1607f08d942aad035d0896e37ba21393aa16","",".csv" +"Dataset\WHO-country_context\who_country_year_long.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","61423","2026-09-03T17:03:57.9180616Z","812640f319be18586858bb447beca3bd3f5be6c6e4b2d42a728a681d1e892910","",".csv" +"Dataset\WHO-country_context\who_indicator_metadata.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","578","2026-09-03T17:03:57.9190587Z","608ada0b69aff50561b7a25acfb34efbce0ee4c99201e269513e736f9627a0de","",".csv" +"Dataset\WHO-country_context\world_bank_country_codes.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","14751","2026-09-03T17:03:53.6470177Z","78a488455afb47146b87d16a74a614207829757887da9c4b7134449d5b004366","",".csv" +"Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","1411796","2026-09-03T17:03:55.9610149Z","b334d353a771de73e0300d0deb365199a95d6eb269f0fdd9955c3885b3b72c6e","",".csv" +"Dataset\WHO-country_context\world_bank_indicator_metadata.csv","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","3856","2026-09-03T17:03:55.9620114Z","4c69a0d27051c7dcdd2e0c9bd9453d9dfeba4e2aa30d807d1682d7435286e0c0","",".csv" +"Dataset\YRBS_National_1991_2023\checksums.csv","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1717","2026-09-03T15:10:39.0692767Z","43b2f99f5d7b6f931df8d759d00b7ae1b5b518375015cc919ed329aadb5a5fdf","",".csv" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\1991_hs_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","49596","2026-09-05T06:05:39.0183684Z","1879b3f9f856259277d7f194623a31a521358bf9d0babee448319ed0bff6fc43","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\1993_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","92197","2026-09-05T06:05:36.4276331Z","126e316f002f1dca0da44a275214a894b378851c45e310620b5b7f68658858a8","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\1995_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","67554","2026-09-05T06:05:33.5366199Z","49a15acb64f8ed293605d7d7ff58c05074fde10ba52c0fb9790e326a4702fe12","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\1997_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","145793","2026-09-05T06:05:31.0036574Z","90e752cfc4a15a28b3817fbc5fb96debeb35a8709214be1e90f624b5906d78e0","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\1999_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","322699","2026-09-05T06:05:12.4409909Z","7a220ace866c63268fc5ea9f0dadf384aacc9146216d508b9bb1e045f0c15fb0","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2001_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","214863","2026-09-05T06:05:09.3256308Z","2a7ae80cdeb4eb0ffcb1f00269d27857d889f0b4c3d23a79394fa4584a2f2e5a","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2003_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","225066","2026-09-05T06:05:06.5067059Z","96cc31f79d0ade61cb5a7392681e33c160a6c6bc474d1123fa000768ae2f5d4d","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2005_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","227216","2026-09-05T06:05:03.1577181Z","d45136f6faeff804039591aafb70d17583b5ea495da71276c9ad366ffd61e1f6","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2007_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","210697","2026-09-05T06:05:00.1380998Z","79fe43c985f1f853f2d9a4d907c5b3f10c46c525a57a53dbb6b5e2a5db91ae83","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2009_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","110940","2026-09-05T06:04:56.6183784Z","7360017ee805881375ded2a79a6ae1e923457a92643df0a01bdff6249acb664c","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2011_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","65922","2026-09-05T06:04:52.6400085Z","9414c19f28550a637e1e96b5b4fede8aaabbe9f0da6a9d8640d072a221478a9d","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2013_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","171284","2026-09-05T06:04:50.1730475Z","d69de8155fb79006e7a9382f864584512e02f65c1d6cd1ad46cbc50f37ad3d7f","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2015_xxh_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","170957","2026-09-05T06:04:47.0311014Z","0f89c5733ce60f0d75dec6ab66b0bd195b98372f8c5f107f85613e4f6ad3b4bb","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2017_yrbs_national_hs_questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","431418","2026-09-05T06:04:44.5226659Z","8d7d926f5ce170bc82e5447663398bb55cb362c1f95dfadd92443079a0fba15a","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2019_YRBS-National-HS-Questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","239960","2026-09-05T06:04:41.6097778Z","dfa2e1d611ff98b65314dbb31dfd4ba966da8700692cbcc9c79adb0517b542e1","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2021-YRBS-National-HS-Questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","152590","2026-09-05T06:04:37.8425905Z","d68d6c5a061b0fba8395b9dd2864abdc13667dbacb6764cb37f2c491c89689b0","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\Questionnaire\2023_YRBS_National_HS_Questionnaire.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","338950","2026-09-05T06:04:34.8966365Z","3c00c06ea82ab8991a8aa757be4dd1ffe694fbe54114fef65bdf98751a58137c","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\2015_yrbs-data-users_guide_smy_combined.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","625734","2026-09-05T05:47:26.9291458Z","0c8edc98ccad82322b33a75a3d0ab6b31f84a3b1c07e53c32349d29e6266a1bd","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\2017_YRBS_Data_Users_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1088143","2026-09-05T05:47:23.6372504Z","3ac5a8b3e62cef9007c5c86f4b8a6eaba15ebd82ef77332335e6670e5387370a","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\2019_National_YRBS_Data_Users_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","929749","2026-09-05T05:46:56.6652194Z","b60d9e9571939a47fab597448cda537e12e8c712cd20cd332d2ebe6c0fcd25b1","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\2021_YRBS_Data_Users_Guide_508.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2215399","2026-09-05T05:46:42.1379788Z","e758264daa61b0e8750401f98c7485ed98eb21983afa87b7fe5f32aa50e2cfe2","dup-0104",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\2023_National_YRBS_Data_Users_Guide508.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1036445","2026-09-05T06:02:17.8843303Z","54b081c797873c1a16d7b6d4be728591667fb7781e799ee2bec9fa6ae3f90a61","dup-0041",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_1991_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","506504","2026-09-05T05:49:20.7138588Z","670935a4f90293af9971f1a20c7b2a0a4a2d9f461be6f5f124b16509eec31b6e","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_1993_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1396379","2026-09-05T05:49:17.8230476Z","f47b72d5e7b75392e5772a2eeed45288297df81bdfe3036058357034e125252e","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_1995_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","465656","2026-09-05T05:49:13.3931945Z","a8fe42f2c4b8492fa5db44a39bf21346d35bd8e040b610c725cd974f677ed8e3","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_1997_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","231675","2026-09-05T05:49:10.6273106Z","68e17bb21378ba3a959023a72119dc0badd1710a879869a71be62e8c91621980","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_1999_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2173532","2026-09-05T05:48:09.7826787Z","c5ae6b25aaab3983cfdffc60291e941812f58c0a24e8fc759ff6dc7dfee82305","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2001_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1379220","2026-09-05T05:48:06.3754664Z","4f6f7f0e394b6ca6ff6418f861716e0dc46d810cc4fe9b4278568a01a3f853e2","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2003_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2168762","2026-09-05T05:47:54.4005685Z","f949be6c25963d301d75d7b51784306f6b55d4dd12e889f1446f51f642893ffa","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2005_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3391825","2026-09-05T05:47:49.0010241Z","23702d3ec472bcb92b57fbf77d8beac87447a6b363f25f37dfd811e370c6d2eb","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2007_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","625327","2026-09-05T05:47:45.2812684Z","777b21d52babba0909b60f98cb14f9c95d26d18f72c247b92b31cd7ba62545cc","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2009_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","985965","2026-09-05T05:47:41.5381593Z","28b3c808589137b9b2a34124023dbf0ea3261e9e4cea74680cc7a6c0416137fa","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2011_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","952636","2026-09-05T05:47:35.8338780Z","1f2f7f8212d5a3f137d8ece86b0ac85732da37855c0337a63ea652bd58b07fe8","",".pdf" +"Dataset\YRBS_National_1991_2023\documentation\UserGuide\YRBS_2013_National_User_Guide.pdf","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1915344","2026-09-05T05:47:29.9355844Z","2cc1f2a95809f99057923fac9facf6c7d886f4af9ccca354000e42f60185300f","",".pdf" +"Dataset\YRBS_National_1991_2023\extracted_mdb\1991\yrbs1991.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","4747264","2006-05-29T22:50:16.0000000Z","f795d65d4c2dc3e3983d2380079050316a4cbde99047b19a251c3ba3345d60dc","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\1993\yrbs1993.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6950912","2006-05-29T22:50:50.0000000Z","2ca85d7c395c042f73a00bf8a7713a62ef2a36d7425a337d21eb54501d27bfd9","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\1995\yrbs1995.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","4644864","2006-05-29T22:51:16.0000000Z","44d1f8cf65059bb5492639ac124d8363dd774e7e23a4ae90c71eae1ef1960f30","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\1997\yrbs1997.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6840320","2006-05-29T22:52:18.0000000Z","5c817310959931ffa72b19f2d363ca9fff9aef4f3dbcf59562f9d4083c0fa859","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\1999\yrbs1999.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","7389184","2006-05-29T22:21:14.0000000Z","98c4d5009ac0521fb70b3ce635ec7c3292c8272dd5013cc4b30a1fb914162b60","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2001\yrbs2001.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6881280","2006-05-29T22:23:52.0000000Z","cc03ccd1bdd0e67b310d89bb7e6d0f80f3c4ee7382fc604b98276f8627a4eb0b","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2003\yrbs2003.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","11165696","2006-05-29T22:24:42.0000000Z","18584c1e47fc3e19018c96c52dc3768d198403d5203a0380bb5907282a2ab68b","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2005\yrbs2005.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","11313152","2006-05-29T22:26:14.0000000Z","efe195059a396f11655446242d676a00eddc1ffe667329d8abae2b1183c5cad9","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2007\yrbs2007.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","19300352","2008-03-24T01:17:14.0000000Z","2fefb8e23df170ea8e6b49a2dea3e01341345a4b3207e16accd34b2f196ec441","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2009\yrbs2009.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","45023232","2010-04-26T21:34:10.0000000Z","cc3b358db3e962aab96847c13656d187a297ccda42868daed3f115560ccb41fb","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2011\yrbs2011.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","21295104","2012-03-18T21:37:04.0000000Z","e66538f4a05dfcc985b5c7f52cecb7b1de9f926cfbb7ab3e602e2c0e027bdbb3","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2013\YRBS2013.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","25538560","2014-01-02T01:34:52.0000000Z","1ac5da17ce1b94ba2b46826ee82bbe7a45d29b978e772ad1b32773d5d6e8128a","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2015\YRBS2015.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","50716672","2018-06-12T22:15:08.0000000Z","28088e68ec6ac9ed8123d50c2055fcfe39d65babf8ac6d6facda49928e4fcbf8","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2017\XXH2017_YRBS_Data.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","47943680","2018-04-30T02:39:28.0000000Z","e28c6b56174e0bb8d37bec96afcc43dc819f35f8aea9b7ab4064e79defb37c4c","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2019\XXH2019_YRBS_Data.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","25825280","2019-11-25T18:03:06.0000000Z","beae213846795bd6cc8961d1517243ac9a11384239ed00a0e6f909641c7ced65","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2021\XXH2021_YRBS_Data.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","32157696","2023-02-09T21:59:02.0000000Z","8ed22a313eeec272998d76e45d5b4c1b4f375809db5a2b5ef97b8936dbb37645","",".mdb" +"Dataset\YRBS_National_1991_2023\extracted_mdb\2023\XXH2023_YRBS_Data.mdb","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","38273024","2024-01-24T07:33:32.0000000Z","04b39e29c8e51b2e5697b6faf427e455c87d70193f01e78aa79b1e1e9ccf6171","",".mdb" +"Dataset\YRBS_National_1991_2023\raw\XXH2017_YRBS_Data.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6334185","2026-09-03T15:10:41.6682392Z","99f7a87f1f936b21674603064118222247e797323377de7c7365e75a26c6f4b9","",".dat" +"Dataset\YRBS_National_1991_2023\raw\XXH2017_YRBS_Data.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3795289","2026-09-05T05:49:58.4185600Z","394f0dffeab88a8920d9c996ab23f3947fd214e6a3846ff6823d14683f065b06","",".zip" +"Dataset\YRBS_National_1991_2023\raw\XXH2019_YRBS_Data.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","5758017","2026-09-03T15:10:43.9772715Z","9bfb3e87c519418eb6bd5b491ecfc723c1b21b85dbe796816fcce6b3cea8ca26","",".dat" +"Dataset\YRBS_National_1991_2023\raw\XXH2019_YRBS_Data.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2110950","2026-09-05T05:49:55.2737880Z","aa5be5a6c4d182869547cf450fb64336d9f9b03ecb79cbb2655a16f5b7792c43","",".zip" +"Dataset\YRBS_National_1991_2023\raw\XXH2021_YRBS_Data.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2733554","2026-09-05T05:49:54.3748858Z","f5eb79107a464a871721ab96ce75637d5bf8374c3b47cc71458837bb8b585a34","dup-0111",".zip" +"Dataset\YRBS_National_1991_2023\raw\XXH2023_YRBS_Data.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","8503569","2026-09-05T05:21:43.0249450Z","0a83cba11dc8a0cdf0043aacf0b80478f86cf93a91a8ecb81a276518e486241a","dup-0003",".dat" +"Dataset\YRBS_National_1991_2023\raw\XXH2023_YRBS_Data.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3306896","2026-09-05T05:21:36.6887990Z","5343eeecf9aee1e50e68510fb05d7142236f67f7a54879a611f04ece39e79936","dup-0040",".zip" +"Dataset\YRBS_National_1991_2023\raw\YRBS_2021_National.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","7289136","2026-09-03T15:11:17.4307320Z","0a8605d7843fba9cb50e7c0c4094089b0ce2793a9988506971fdae3d9f2a04ae","dup-0004",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1991.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1153568","2026-09-03T15:10:48.0192495Z","ed8a86b9a5d767575ee4465946f74514f20fc16bdfa54c9cd87200c22cf7cbc7","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1991.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","479595","2026-09-03T15:10:48.2442786Z","1616ecf629b7a531f01a2e6dc11b0418c6c532283013c558b14db592bd49af8d","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs1993.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1792560","2026-09-03T15:10:48.9676438Z","4298a0d9d15baccc1602da6a27aab468a3e032acef691d3c264074cdd9bf8e09","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1993.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","690738","2026-09-03T15:10:49.2763197Z","6a2020362ac8080a55331d02f7aa447e215f5409d304b5de732935cb888b45cf","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs1995.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1232152","2026-09-03T15:10:49.8194147Z","e76e55e6c37adc4ef81192c6ba026b4d7c7b03fc598c25cc63393be28a06c100","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1995.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","471180","2026-09-03T15:10:50.0366917Z","44691919c1c01814bef0699d9fbd356fa2efcf8b659a74097d68d43af7631812","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs1997.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1821345","2026-09-03T15:10:50.7667153Z","03b654a5ffd0693b5f8e69ff1c2824816964ae4c5da885c3960074591a30d704","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1997.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","688931","2026-09-03T15:10:51.0921425Z","560682aa139f048e358faea53e32ced1ee295f07161773295f1fae73d37d7785","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs1999.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1949323","2026-09-03T15:10:51.9165974Z","1504ad1c303b701fb6552a68ecfa5fe95abfd54c85dfd7c7e23e95676c2e7d93","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs1999.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","757147","2026-09-03T15:10:52.2329234Z","dd1604f6f75ad54fb13ce910cd987a956adfdd9d01536bbe99091893b75ced01","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2001.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1768130","2026-09-03T15:10:53.1935367Z","b22d6ca658a553ecab134eda670bf9d84281281c0be732a56c8a6a28d14830fc","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2001.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","697164","2026-09-03T15:10:53.5028409Z","717d544536324e8002b61d83d38c70887e154a8557ddda9a83018c0cd4252ec1","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2003.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3590504","2026-09-03T15:10:54.9608951Z","4d01d3a91ccfddfb129e8c414c60fdc848270056b1e956717f7c7c24718abc75","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2003.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1251996","2026-09-03T15:10:55.5339400Z","20c359d9d773105361f0bed01eeaa7ad5f65836f9ea115c9e351645ab7b59565","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2005.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","5441547","2026-09-03T15:10:57.7224467Z","da3fe2f5059cbb53e69b5f1a1d0a15e15e86e6efbfa6f4548af1c7b931d523f8","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2005.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1350676","2026-09-03T15:10:58.3325536Z","59f2f538be08a33e6333ae81cdb3a2e5063d1d821ea1e6cb2295d8b4b9464790","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2007.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","5433867","2026-09-03T15:11:00.5157710Z","3a5c6ea445fa2c553133e6c69d4c2c21fcb4c9bdeea2ce19de63f2319c24ad60","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2007.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1618938","2026-09-03T15:11:01.2357799Z","8f9b9d203aeff6758f1c2abcb95ca54c004c8afeb74cf0113794af8f33dbc940","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2009.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6498360","2026-09-03T15:11:03.8421915Z","d66092c3b4e1e9e89bc3c9548481718206d4cbc9d00fdd51e4f03e72c9abbdc9","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2009.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3899326","2026-09-03T15:11:05.3956410Z","3cdeb65e51f9a94ae666788b84e220ace8f3fcb7566aecfa2dfdb7f76d8d0646","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2011.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6031175","2026-09-03T15:11:07.8704242Z","cf36e00a44b45874727ae0fff6aba195a0b774e9fbf23bb77198a5492f8f754d","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2011.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","1811961","2026-09-03T15:11:08.6000668Z","1aa5757f6e56eb791d23f959ada129d27754f6d975dbb28e268a104fbbf70e8c","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2013.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","5582613","2026-09-03T15:11:10.8935330Z","46fa5d52941331c6fec2de5ad4f7bce6ab5e12e0e07af46feaebdf9d686eb5c3","",".dat" +"Dataset\YRBS_National_1991_2023\raw\YRBS2013.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","2164647","2026-09-03T15:11:11.8259844Z","b5ab5afef36d9465698b3f71bd8153e00c1e08808292b1e984ac6c31f4af0b94","",".zip" +"Dataset\YRBS_National_1991_2023\raw\yrbs2015.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","6608952","2026-09-03T15:11:14.5141161Z","d557f559b8be4301f1b2e790cab3cea5cc15ca10c119b55f72eadc3692d0332b","",".dat" +"Dataset\YRBS_National_1991_2023\raw\yrbs2015.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","4170767","2026-09-05T06:12:36.3536942Z","726283c7bc34785722f1af771b0e970e9fd39a7d7d574870f4241425ac2433a6","",".zip" +"Dataset\YRBS_National_1991_2023\README.md","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer 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candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","17121","2026-09-09T13:35:20.8699269Z","11f3895710ca4bac72f6aaa0c3ed50a1b65b525c7ea56971e51a8490dbf71d65","dup-0010",".sas" +"Dataset\YRBS_National_1991_2023\source_archive\2021_cdc_download\2021XXH-SAS-Input-Program.sas","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","17031","2026-09-09T13:35:20.9333740Z","f5bd4bfaef2c0e6244178649a1b251cef7910ba2f8379a7c97339712b5a00b0d","dup-0110",".sas" +"Dataset\YRBS_National_1991_2023\source_archive\2021_cdc_download\2021XXH-SPSS.sps","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer 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candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","30337","2026-09-09T13:35:18.1478194Z","e314d685a330946c385fb68c7e521794ccc44ba050f44be190fd3a0392d08288","dup-0099",".sps" +"Dataset\YRBS_National_1991_2023\source_archive\2023_cdc_download\XXH2023_YRBS_Data.dat","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","8503569","2026-09-09T13:35:16.3026352Z","0a83cba11dc8a0cdf0043aacf0b80478f86cf93a91a8ecb81a276518e486241a","dup-0003",".dat" +"Dataset\YRBS_National_1991_2023\source_archive\2023_cdc_download\XXH2023_YRBS_Data.zip","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","3306896","2026-09-09T13:35:16.8848225Z","5343eeecf9aee1e50e68510fb05d7142236f67f7a54879a611f04ece39e79936","dup-0040",".zip" +"Dataset\YRBS_NEW\2017XXH_Formats_Program.sas","YRBS documentation/import workspace","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","layout and questionnaire verification","none","audit_required","public_use_terms_review_required","17372","2026-09-14T21:27:37.7484170Z","e3b25286d0e7eb9d32205ab5183d69ea40027f488e4bff9042a6a08d392e1f96","",".sas" +"Dataset\YRBS_NEW\2017XXH_SAS_Input_Program.sas","YRBS documentation/import workspace","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","layout and questionnaire 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candidate","none","archive_only","public_use_terms_review_required","24036","2026-09-17T09:17:01.9170562Z","f8d226acc8a16c25c3b7d27ff180c7f47ab651f17eb10048a45c32182a90025e","",".sas" +"Dataset\YRBS_Site_2019_Untouched\source_archive\2019_SADC_SAS_Formats_Program.sas","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","25126","2026-09-17T09:17:07.9791096Z","ecd155f4187ff48cd94a6018ab62144e3f285e9fc1f555ba7268a17668e110bb","",".sas" +"Dataset\YRBS_Site_2019_Untouched\source_archive\2019_YRBS_SADC_Documentation.pdf","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","2127833","2026-09-17T09:17:13.9326189Z","4ee1e87ce2fd3e25dd5f60dbccc2c7111c13a2d07a23280ac949717a1a020d4e","",".pdf" +"Dataset\YRBS_Site_2019_Untouched\source_archive\2019-SADC_District SAS Input Program.sas","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","24034","2026-09-17T09:16:50.0648220Z","d0932da556dacc89f56e5a64cbfbe3be30ee24770952f3e2915053991f7d7388","",".sas" +"Dataset\YRBS_Site_2019_Untouched\source_archive\SADC_2019_District.zip","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","93455504","2026-09-17T09:05:37.9504863Z","62c39cec81f1fac9cb43aa13d46c60a89270f1f0544c7c845d1de015dac58023","",".zip" +"Dataset\YRBS_Site_2019_Untouched\source_archive\SADC_2019_State_A_M.zip","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","143984769","2026-09-17T08:59:05.6287642Z","7869f462afbd44a582c1356bab6b8f4c686767065acc318704bcb0266f183244","",".zip" +"Dataset\YRBS_Site_2019_Untouched\source_archive\SADC_2019_State_N_Z.zip","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","133515086","2026-09-17T08:55:04.1644658Z","5bb26c0fc8432e1d1b4533f5eaf779c58767763eb11a588c4b4ce9d0d9a8be36","",".zip" diff --git a/research/audit/data_source_registry.csv b/research/audit/data_source_registry.csv new file mode 100644 index 0000000..279106b --- /dev/null +++ b/research/audit/data_source_registry.csv @@ -0,0 +1,19 @@ +path_prefix,dataset_group,authority,source_url,project_role,canonical_analysis_entry,readiness,license_distribution_status,notes +"Dataset\GSHS-全球学生健康调查数据\GSHS","GSHS","World Health Organization GSHS / local curated delivery","https://extranet.who.int/ncdsmicrodata/index.php/catalog/GSHS","cross-national development candidate","Dataset\GSHS-全球学生健康调查数据\GSHS\01_data\GSHS.csv","audit_required","restricted_redistribution_review_required","Use requires survey-level source and current WHO terms; local package is not an official WHO republication." +"Dataset\GSHS-全球学生健康调查数据","GSHS source archive","WHO GSHS and local source archive","https://extranet.who.int/ncdsmicrodata/index.php/catalog/GSHS","provenance archive","none","archive_only","restricted_redistribution_review_required","Do not analyze alternate copies until matched to the canonical GSHS entry." +"Dataset\GSHS_Public_Aux","GSHS public auxiliary","WHO GSHS public documentation","https://extranet.who.int/ncdsmicrodata/index.php/catalog/GSHS","documentation candidate","none","documentation_only","review_current_source_terms","Auxiliary files require item-level source verification before use." +"Dataset\YRBS_National_1991_2023","YRBS national raw archive","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","questionnaire and temporal transfer candidate","Dataset\YRBS_National_1991_2023\raw","raw_verified_by_local_inventory","public_use_terms_review_required","Seventeen national survey years are inventoried; analysis should use verified year-specific layouts." +"Dataset\YRBS_NEW","YRBS documentation/import workspace","US CDC YRBS","https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","layout and questionnaire verification","none","audit_required","public_use_terms_review_required","Not a canonical analysis entry; use to validate official layouts and documentation." +"Dataset\YRBS_Site_2019_Untouched","YRBS site 2019 untouched archive","US CDC YRBS site data","https://www.cdc.gov/yrbs/data/index.html","external/site-level candidate","none","archive_only","public_use_terms_review_required","Preserve untouched; not part of the primary national-series entry." +"Dataset\YRBS_SADC","YRBS SADC candidate","source requires file-level confirmation","","external candidate","none","conditional","unknown_review_required","Retained because scripts/audit references exist; source and population must be confirmed before use." +"Dataset\ABES_2021","ABES 2021","US CDC Adolescent Behaviors and Experiences Survey","https://www.cdc.gov/abes/","external pandemic-period candidate","none","conditional","public_use_terms_review_required","Not in the core three-survey plan; keep separate unless formally added." +"Dataset\可直接分析数据包_NSDUH_YRBS","NSDUH/YRBS processed delivery","SAMHSA NSDUH DAS API and US CDC YRBS programs","https://datatools.samhsa.gov/; https://www.cdc.gov/yrbs/data/national-yrbs-datasets-documentation.html","processed candidate inputs","Dataset\可直接分析数据包_NSDUH_YRBS\NSDUH\nsduh_2021_2024_full.parquet; Dataset\可直接分析数据包_NSDUH_YRBS\YRBS\full_by_year","conditional","upstream_terms_review_required","NSDUH 2021-2024 and YRBS 1991-2019 are candidate entries; YRBS 2021/2023 remain quarantined." +"Dataset\可直接分析数据包_NSDUH_YRBS.zip","NSDUH/YRBS processed delivery archive","local archive of processed delivery","","provenance archive","none","archive_only","inherits_upstream_terms","Exact relationship to unpacked delivery is determined by SHA-256/contents audit." +"Dataset\NSDUH","NSDUH source and documentation workspace","US SAMHSA NSDUH","https://datatools.samhsa.gov/","external validation source archive","none","audit_required","public_use_terms_review_required","Contains source materials and web/documentation artifacts; do not select files by name alone." +"Dataset\National Survey on Drug Use and Health","NSDUH bulk archive","US SAMHSA NSDUH","https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health","external validation source archive","none","archive_only","public_use_terms_review_required","Large bulk/source archive; canonical analysis entry is not assigned." +"Dataset\National Survey on Drug Use and Health.zip","NSDUH bulk archive zip","US SAMHSA NSDUH","https://www.samhsa.gov/data/data-we-collect/nsduh-national-survey-drug-use-and-health","provenance archive","none","archive_only","public_use_terms_review_required","Preserve until duplicate and reference audits are complete." +"Dataset\NSDUH-2021_2024-DS0001-bndl-data-r_v1","NSDUH 2021-2024 R bundle","US SAMHSA NSDUH","https://datatools.samhsa.gov/","external validation source candidate","none","conditional","public_use_terms_review_required","Single delivered bundle; inspect official guide and population restrictions before use." +"Dataset\NSDUH2024PUF","NSDUH 2024 public-use file","US SAMHSA NSDUH","https://datatools.samhsa.gov/","external validation source candidate","none","conditional","public_use_terms_review_required","Confirm exact format, guide and linkage to processed 2024 data." +"Dataset\WHO-country_context","country-year context","World Bank API and WHO data services","https://api.worldbank.org/; https://ghoapi.azureedge.net/api/","later explanatory/context layer","Dataset\WHO-country_context\world_bank_country_year_1990_2025.csv","conditional","source_specific_terms_review_required","Do not add before the measurement layer; preserve source code, observation year and time-match lag." +"Dataset\_organized\external_candidates\Global_Mind_Project","Global Mind external candidate","Sapien Labs / Global Mind Project","","external candidate","none","hold","unknown_review_required","Outside the current GSHS/YRBS/NSDUH core; no analysis until provenance and terms are confirmed." +"Dataset\_organized","organization metadata","local project organization","","project metadata","none","documentation_only","not_applicable","Contains organization and duplicate-audit records in addition to external candidates." diff --git a/research/audit/dataset_group_summary.csv b/research/audit/dataset_group_summary.csv new file mode 100644 index 0000000..5d78544 --- /dev/null +++ b/research/audit/dataset_group_summary.csv @@ -0,0 +1,18 @@ +"dataset_group","files","bytes","readiness","license_distribution_status" +"ABES 2021","7","7391690","conditional","public_use_terms_review_required" +"country-year context","32","42150439","conditional","source_specific_terms_review_required" +"Global Mind external candidate","3","1656016876","hold","unknown_review_required" +"GSHS","61","779756986","audit_required","restricted_redistribution_review_required" +"GSHS public auxiliary","4","278047","documentation_only","review_current_source_terms" +"NSDUH 2021-2024 R bundle","1","138584076","conditional","public_use_terms_review_required" +"NSDUH 2024 public-use file","1","2152799","conditional","public_use_terms_review_required" +"NSDUH bulk archive","85","4844091900","archive_only","public_use_terms_review_required" +"NSDUH bulk archive zip","1","4844112316","archive_only","public_use_terms_review_required" +"NSDUH source and documentation workspace","335","306817254","audit_required","public_use_terms_review_required" +"NSDUH/YRBS processed delivery","33","317135421","conditional","upstream_terms_review_required" +"NSDUH/YRBS processed delivery archive","1","138448123","archive_only","inherits_upstream_terms" +"organization metadata","2","2289","documentation_only","not_applicable" +"YRBS documentation/import workspace","51","871464","audit_required","public_use_terms_review_required" +"YRBS national raw archive","99","525338699","raw_verified_by_local_inventory","public_use_terms_review_required" +"YRBS SADC candidate","7","929903085","conditional","unknown_review_required" +"YRBS site 2019 untouched archive","11","5934213128","archive_only","public_use_terms_review_required" diff --git a/research/audit/duplicate_summary.csv b/research/audit/duplicate_summary.csv new file mode 100644 index 0000000..49cacae --- /dev/null +++ b/research/audit/duplicate_summary.csv @@ -0,0 +1,118 @@ +"duplicate_group","file_count","redundant_copies","bytes_each","potential_redundant_bytes","sha256","paths" +"dup-0001","3","2","991","1982","01981e84ec539c1fe4420cde4391b081b2d66ee046895b9546f704e77e94d96a","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-inlineeq11-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-inlineeq9-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-inlineeq2-2023.gif" +"dup-0002","2","1","1091","1091","0715719ece33268d990c0cb34b72cdd2150c57efbbea5424eeb6baa7c4efa22b","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-inlineeq16-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-inlineeq13-2022.gif" +"dup-0003","2","1","8503569","8503569","0a83cba11dc8a0cdf0043aacf0b80478f86cf93a91a8ecb81a276518e486241a","Dataset\YRBS_National_1991_2023\raw\XXH2023_YRBS_Data.dat | Dataset\YRBS_National_1991_2023\source_archive\2023_cdc_download\XXH2023_YRBS_Data.dat" +"dup-0004","2","1","7289136","7289136","0a8605d7843fba9cb50e7c0c4094089b0ce2793a9988506971fdae3d9f2a04ae","Dataset\YRBS_National_1991_2023\raw\YRBS_2021_National.dat | Dataset\YRBS_National_1991_2023\source_archive\2021_cdc_download\XXH2021_YRBS_Data.dat" +"dup-0005","2","1","436252","436252","0ac7033dbcfe094acfa62d5bdba98331f86f93ed6b8c25ec57447b132720e3d6","Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\gtm.js.下载 | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\gtm.js.下载" +"dup-0006","3","2","1837","3674","0b56ec123ed2d2ccd0ff748032387e8dc97bc02c96c25723f957fe6eccb62691","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-tab3-2eq1-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq1-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq1-2023.gif" +"dup-0007","2","1","1536","1536","0e240856a2a5d3d65d1fedc49f577af57fabcad853dabd5221162cbc768fe058","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-eq3-2-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-eq3-2-2022.gif" +"dup-0008","3","2","1889","3778","0fb73c69129db212caa16866a86d83f4b72956eb8bf114e9fbc011adcb1310e5","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-tab3-2eq7-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq7-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq7-2023.gif" +"dup-0009","3","2","5649","11298","103585bfb64e2fef8a7c9db5797cc308c484b7b71f7836bd7038132327c08175","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\iconochive.css | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\iconochive.css | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\iconochive.css" +"dup-0010","2","1","17121","17121","11f3895710ca4bac72f6aaa0c3ed50a1b65b525c7ea56971e51a8490dbf71d65","Dataset\YRBS_National_1991_2023\source_archive\2021_cdc_download\2021XXH-Formats-Program.sas | Dataset\YRBS_NEW\2021XXH-Formats-Program.sas" +"dup-0011","3","2","777","1554","19a790cc1351650972148416e59edc7be277b8d9bf5c6244c8338046224810a9","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\scaltop.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\scaltop.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\scaltop.gif" +"dup-0012","3","2","802","1604","1ac738544eac8f12e25901b4b3329a33c0d43621e1bd1c19cac8308cab4b5036","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-ftn41eq5-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-ftn37eq5-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-ftn37eq5-2023.gif" +"dup-0013","2","1","142730","142730","1cae7337137d60cbd8b2e868bc31db7298ef69ec1a90e412d84e3f5e6d8e20d8","Dataset\National Survey on Drug Use and Health\NSDUH_2021\NSDUH2021PUF_Supplement.pdf | Dataset\NSDUH\NSDUH2021PUF_Supplement.pdf" +"dup-0014","2","1","766","766","203361feb1bc99ae252d4c2b36b61a6c0c30a0dc5fa58510e0291476d6a19bb2","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-ftn41eq6-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-ftn37eq6-2022.gif" +"dup-0015","2","1","988","988","2182d395986471387432eac0e3e3eda86e8f050e13ef88e082764e35fbf0b6ef","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-rr2i-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-rr2i-2022.gif" +"dup-0016","3","2","903","1806","21a8f20a0906b874d54ab2d6646a2074eab2b98cdf60c8e63864c838ef6d9ba6","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-ftn41eq2-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-ftn37eq2-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-ftn37eq2-2023.gif" +"dup-0017","3","2","873","1746","21d67cd9e0b9e39f95aa44e1c943ed5411a7daf8b5cc0f8905c2b4ade5e5c129","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-xbar-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-xbar-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-xbar-2023.gif" +"dup-0018","2","1","2309","2309","239c5dafc7b2b4eeb1aec36a55755964dd2216950891028913521569dfb83509","Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\embed.js.下载 | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\embed.js.下载" +"dup-0019","3","2","733","1466","2eef5cc7a05ca41b5bb0d6e0525ac387609ab91f2548b37653e1a5d8341eac57","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\scaltoc.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\scaltoc.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\scaltoc.gif" +"dup-0020","3","2","857","1714","3287daec529ef279343216807a021e7362c6c17b6f6469031615e1f8e376d06b","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-pi-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-pi-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-pi-2023.gif" +"dup-0021","2","1","2131","2131","339a331e6f1d38d6363b34f37d1ab8b6631d7abb68787d7b4b0efbc218901e61","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-eq3-5-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-eq3-5-2022.gif" +"dup-0022","3","2","976","1952","3423023e7f181fede528b403dda90ce05070349c8b3da0368a1a2f07f0955a74","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-tab3-2eq5-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq5-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-tab3-2eq5-2023.gif" +"dup-0023","2","1","2469","2469","35083abcbc5a1693cec76ea9c30cd2c530c9f72c1251bc34fd2f13d49ca4d628","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-eq3-8-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-eq3-8-2022.gif" +"dup-0024","2","1","925","925","35733edcafc8c589f74ea866986d875df90b17966b662c6e379bade0cc40b1b9","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-phatsub1-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-phatsub1-2022.gif" +"dup-0025","2","1","884","884","37a1da8f93895852d3ae3c552dc561b2912222960a9b0d71d9bc55c094d804c0","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-lambda-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-lambda-2022.gif" +"dup-0026","3","2","902","1804","3ad5a34afe7d14782bd3b41a665da67fe225d719f2f4631c2c8015350a0f8cfb","Dataset\NSDUH\2021 National Survey on Drug Use and Health_ Methodological Summary and Definitions_files\msr-pisub0-2021.gif | Dataset\NSDUH\2022 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-pisub0-2022.gif | Dataset\NSDUH\2023 National Survey on Drug Use and Health (NSDUH)_ Methodological Summary and Definitions_files\msr-pisub0-2023.gif" +"dup-0027","3","2","10252","20504","3e2bb12dd78408c9371fb23c028385d4b4319f32951f23025eacad7c3f9b157a","Dataset\NSDUH\2021 National Survey on Drug 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+- Python：项目内 CPython 3.12.14 与 `.venv-research`，195 个包已锁定。 +- R：项目内 R 4.6.1 与 `.r-library/4.6`，renv 锁定 119 个包；全安装表 134 行。 +- CUDA：系统 CUDA Toolkit 13.4.2 / nvcc 13.4.92；MSVC 14.44；端到端 kernel 编译和 GPU 运行通过。 +- GPU Python：PyTorch 2.14.0+cu130 在 RTX 5060 Ti 上通过矩阵运算。 + +环境就绪不解除研究设计门槛：正式模型仍必须等待相应阶段的题库、可识别性、冻结验证方案和 Gate 判定。 + +## 2. 硬件与系统 + +| 项目 | 核验结果 | 判定 | +|---|---|---| +| 操作系统 | Microsoft Windows 10.0.26100，x64 | 可用 | +| 逻辑处理器 | 28 | 可用 | +| 物理内存 | 31.82 GiB | 中等规模处理可用；大表仍须分块/列式/稀疏化 | +| GPU | NVIDIA GeForce RTX 5060 Ti，16,311 MiB VRAM，compute capability 12.0 | 可用 | +| NVIDIA 驱动 | 596.36 | 可用；未被 Toolkit 安装器替换 | +| 项目盘 E: | 补强后可用约 250.48 GiB | 当前项目有余量；继续约束缓存与中间产物 | + +## 3. 核心工具 + +| 工具 | 版本/路径 | 验收 | +|---|---|---| +| 项目 Python | CPython 3.12.14；`E:\Model\.venv-research` | 通过 | +| 系统 Python | 3.13.7 | 保留，不用于正式项目环境 | +| R / Rscript | R 4.6.1 UCRT x64；`E:\Model\.tools\R-4.6.1` | 通过 | +| CUDA Toolkit | 13.4.2；nvcc V13.4.92 | 通过 | +| MSVC host compiler | Visual Studio 2022 Community，MSVC 14.44.35207 | 通过 | +| Git | 2.51.0.windows.1 | 已安装；`E:\Model` 不是 Git 仓库 | +| Pandoc | 3.11 | 可用 | + +## 4. Python 研究环境 + +### 4.1 关键版本 + +| 类别 | 包与版本 | +|---|---| +| 数值/表格 | numpy 2.5.3；scipy 1.18.1；pandas 3.0.6；polars 1.44.2；pyarrow 25.0.1；duckdb 1.5.5 | +| 统计 | statsmodels 0.15.0；scikit-learn 1.6.1；xgboost 3.4.1 | +| Bayesian | PyMC 6.3.2；PyTensor 3.3.2；ArviZ 1.3.0；nutpie 0.16.11；JAX 0.11.2；NumPyro 0.22.0；BlackJAX 1.6.2 | +| 心理测量 | semopy 2.3.11；factor-analyzer 0.5.1；pingouin 0.6.1；girth 0.8.0 | +| GPU/NLP | torch 2.14.0+cu130；torchvision 0.29.0+cu130；transformers 5.17.0；sentence-transformers 6.1.0；tabpfn 9.0.0 | + +scikit-learn 固定在 1.6.1，以保留 factor-analyzer 0.5.1 所需的弃用兼容层。torchaudio 因项目无音频任务且官方 cu130 索引没有同代 2.14.0 Windows wheel而未纳入。 + +### 4.2 运行验收 + +`research/environment/smoke-test-result.json` 记录： + +- 25 个目标模块全部导入；`pip check` 无断裂依赖。 +- PyMC/nutpie 完成最小后验采样。 +- semopy CFA 与 factor-analyzer 拟合通过。 +- TabPFN、Transformers 与 Sentence Transformers 导入通过。 +- PyTorch 在 RTX 5060 Ti 上执行 CUDA 矩阵乘法，结果有限。 + +50 draws 的 PyMC 测试仅证明环境可运行，不用于统计诊断或研究结论。 + +## 5. R 研究环境 + +### 5.1 关键版本 + +| 类别 | 包与版本 | +|---|---| +| 复杂抽样 | survey 4.5；srvyr 1.3.1 | +| IRT/SEM | mirt 1.47；lavaan 0.7-2；semTools 0.5-9；psych 2.6.5 | +| 数据交换 | arrow 25.0.1；haven 2.5.5；data.table 1.18.6.1；readr 2.2.0 | +| 复现 | renv 1.2.4；targets 1.12.0 | + +### 5.2 运行验收 + +`research/environment/r-smoke-test-result.json` 记录： + +- 19/19 顶层包导入通过。 +- survey 复杂抽样加权均值返回有限估计。 +- lavaan CFA 明确收敛。 +- mirt 一维 2PL 在已知生成数据（500 人、8 题）上明确收敛。 +- Arrow Parquet 写入/读回成功。 + +## 6. CUDA Toolkit 验收 + +- 官方网络安装器 MD5：`a0810494c821437671d6e68e66f59daf`。 +- `nvcc --version`：CUDA 13.4，V13.4.92。 +- `cuda-smoke-build.cmd` 在 MSVC 环境中以 `sm_120` 编译 `cuda-smoke.cu`。 +- 生成程序在 GPU 上执行 kernel，并输出 `cuda_smoke_result=42`。 +- Toolkit 与 PyTorch wheel 的 CUDA runtime 版本可以不同；项目当前 PyTorch wheel 使用 cu130，已经独立通过 GPU 回归。 + +## 7. 缓存、存储与数据边界 + +- 所有可控运行缓存定向到 `E:\Model\.cache` 或 `E:\Model\.uv-cache`，避免受限用户目录。 +- `.venv-research` 约 4.39 GiB；`.r-library` 约 0.23 GiB。 +- `Dataset` 有 734 个文件，总计约 19.06 GiB；继续使用 Parquet/Arrow、分块和稀疏/长表策略。 +- 嵌入模型权重尚未冻结；模型 ID、revision、许可、语言覆盖和缓存哈希须在阶段 5 前另行审计。 + +## 8. 能力门槛 + +| 能力 | 当前状态 | 仍需满足的研究门槛 | +|---|---|---| +| 元数据/题库整理 | `ready` | 阶段 1 来源双重核查 | +| 共现矩阵/锚定图 | `ready_with_limits` | 分块/稀疏实现与阶段 2 可识别性审计 | +| 复杂抽样描述/方差 | `ready` | 冻结 estimand、权重、分层与 PSU 处理 | +| SEM/IRT 审计 | `ready` | 二元/有序题必须用适当模型，不套连续 CFA 模板 | +| PyMC 分层模型/SBC | `ready` | 等阶段 3 冻结模拟与诊断标准 | +| 本地文本嵌入原型 | `ready_with_limits` | 先冻结模型/许可/语言覆盖，隔离测试证据 | +| CUDA 自定义扩展 | `ready` | 使用 CUDA 13.4 + MSVC 14.44；保留编译日志 | + +## 9. 可复现产物 + +| 产物 | SHA-256 | +|---|---| +| `python-requirements.lock.txt` | `ab8b3162673357891b91fcad7c917fc51cb0a78e27c7e7b979be55c6178699d6` | +| `renv.lock` | `e953f512597b29c233ba5106420f3486e7bb59eaa68d59d3a37ce5a4e2a819f6` | +| `r-installed-packages.csv` | `6fd8ee5c4c0b86a7f1cb5ab43b998bde15e206d4759757787aa7ca91cc110fc9` | +| `r-session-info.txt` | `f091c2b80df38e485ca6907d2376d8340266c40ea6f1767aaaa6e4a67dc4739d` | +| `r-package-versions.json` | `20847cfc813f5450804ca3c67d9950a313d7813d21852671985e6a3c52e3c3f5` | +| `smoke-test-result.json` | `36fe9596221d6bcb9a4a913d6dc870afa1be41cd427d11c9a300459640d14cd7` | +| `r-smoke-test-result.json` | `164cf3587bb0b1df34b9687d09e148f2b262faddc590d9dde61478c847da6779` | + +使用与恢复步骤见 `research/environment/README.md`。 + +## 10. 输入绑定 + +| 输入 | SHA-256 | +|---|---| +| `Document/research-workflow.md` | `5606e8b10064c980a4a08a91ab7e8db73b04e5077d3440d4f26a40dbf90709e5` | +| `research/audit/data_manifest.csv` | `0427a19c3287a2691edbd9a4486917989fe369fcb0f729a01477ce27cd8f98f2` | diff --git a/research/environment/README.md b/research/environment/README.md new file mode 100644 index 0000000..3dd5bf0 --- /dev/null +++ b/research/environment/README.md @@ -0,0 +1,48 @@ +# 研究环境使用说明 + +## 激活 + +在项目根目录的 PowerShell 中运行： + +```powershell +. .\research\environment\activate-research.ps1 +``` + +该脚本会激活 `.venv-research`，设置项目内 R、R library、CUDA 13.4，并把 uv、renv、PyTensor、Numba、Matplotlib、Skrub、Hugging Face 与 Torch 缓存定向到项目目录。 + +## 验收 + +```powershell +python .\research\environment\smoke_test.py +Rscript .\research\environment\r-smoke-test.R +cmd.exe /d /c .\research\environment\cuda-smoke-build.cmd +``` + +期望结果：Python JSON 中所有 imports/tests 为 `passed`；R JSON 中所有 imports/tests 为 `passed` 且 mirt `converged=true`；CUDA 输出 `cuda_smoke_result=42`。 + +## Python 恢复 + +Python 版本固定为 3.12 系列，完整包版本见 `python-requirements.lock.txt`。Torch 使用 PyTorch 官方 CUDA 13.0 索引： + +```powershell +uv python install 3.12 --install-dir .python --no-registry --no-bin --cache-dir .uv-cache +uv venv .venv-research --python .python\cpython-3.12.14-windows-x86_64-none\python.exe --seed --no-project --cache-dir .uv-cache +uv pip install --python .venv-research\Scripts\python.exe --requirements research\environment\python-requirements.lock.txt --index https://download.pytorch.org/whl/cu130 --index-strategy unsafe-best-match --cache-dir .uv-cache +``` + +若 3.12 补丁版本更新，第二条命令应使用实际安装目录，不要硬编码旧补丁版本。 + +## R 恢复 + +R 版本为 4.6.1。顶层包安装入口为 `install-r-packages.R`；完整依赖恢复使用 `renv.lock`。先激活环境，再运行： + +```powershell +Rscript -e "renv::restore(lockfile='research/environment/renv.lock', library='.r-library/4.6', prompt=FALSE)" +``` + +## 约束 + +- 正式分析只使用上述项目环境；不要把系统 Python 3.13 的包状态当作项目依赖。 +- `smoke-test-result.json` 的短链 PyMC 采样只用于环境验收。 +- `cuda-smoke.exe` 是可重建产物；源码和构建脚本才是复现入口。 +- 嵌入模型权重尚未冻结，下载前仍须执行模型许可与版本审计。 diff --git a/research/environment/activate-research.ps1 b/research/environment/activate-research.ps1 new file mode 100644 index 0000000..30053b9 --- /dev/null +++ b/research/environment/activate-research.ps1 @@ -0,0 +1,25 @@ +$ProjectRoot = (Resolve-Path (Join-Path $PSScriptRoot "..\..")).Path +$CacheRoot = Join-Path $ProjectRoot ".cache" + +$env:LOCALAPPDATA = Join-Path $CacheRoot "localappdata" +$env:MPLCONFIGDIR = Join-Path $CacheRoot "matplotlib" +$env:NUMBA_CACHE_DIR = Join-Path $CacheRoot "numba" +$env:SKB_DATA_DIRECTORY = Join-Path $CacheRoot "skrub" +$env:HF_HOME = Join-Path $CacheRoot "huggingface" +$env:TORCH_HOME = Join-Path $CacheRoot "torch" +$env:XDG_CACHE_HOME = $CacheRoot +$env:UV_CACHE_DIR = Join-Path $ProjectRoot ".uv-cache" +$env:RENV_PATHS_ROOT = Join-Path $CacheRoot "renv" +$env:PYTENSOR_FLAGS = "base_compiledir=$(Join-Path $CacheRoot 'pytensor')" + +$env:R_HOME = Join-Path $ProjectRoot ".tools\R-4.6.1" +$env:R_LIBS_USER = Join-Path $ProjectRoot ".r-library\4.6" +$env:CUDA_PATH = "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4" +$env:Path = "$(Join-Path $env:R_HOME 'bin');$(Join-Path $env:CUDA_PATH 'bin');$env:Path" + +# Windows R uses UCRT/UTF-8 and does not recognize the POSIX locale name C.UTF-8. +Remove-Item Env:LANG -ErrorAction SilentlyContinue +Remove-Item Env:LC_ALL -ErrorAction SilentlyContinue +Remove-Item Env:LC_CTYPE -ErrorAction SilentlyContinue + +& (Join-Path $ProjectRoot ".venv-research\Scripts\Activate.ps1") diff --git a/research/environment/cuda-smoke-build.cmd b/research/environment/cuda-smoke-build.cmd new file mode 100644 index 0000000..b1eb3b0 --- /dev/null +++ b/research/environment/cuda-smoke-build.cmd @@ -0,0 +1,9 @@ +@echo off +call "C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Auxiliary\Build\vcvars64.bat" >nul +if errorlevel 1 exit /b %errorlevel% + +"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v13.4\bin\nvcc.exe" -arch=sm_120 -Xcompiler=/wd4819 "E:\Model\research\environment\cuda-smoke.cu" -o "E:\Model\research\environment\cuda-smoke.exe" +if errorlevel 1 exit /b %errorlevel% + +"E:\Model\research\environment\cuda-smoke.exe" +exit /b %errorlevel% diff --git a/research/environment/cuda-smoke.cu b/research/environment/cuda-smoke.cu new file mode 100644 index 0000000..13a216c --- /dev/null +++ b/research/environment/cuda-smoke.cu @@ -0,0 +1,34 @@ +#include + +#include + +__global__ void increment(int* value) { + *value += 1; +} + +int main() { + int host_value = 41; + int* device_value = nullptr; + + if (cudaMalloc(&device_value, sizeof(int)) != cudaSuccess) { + return 1; + } + if (cudaMemcpy(device_value, &host_value, sizeof(int), cudaMemcpyHostToDevice) != cudaSuccess) { + cudaFree(device_value); + return 2; + } + + increment<<<1, 1>>>(device_value); + if (cudaDeviceSynchronize() != cudaSuccess) { + cudaFree(device_value); + return 3; + } + if (cudaMemcpy(&host_value, device_value, sizeof(int), cudaMemcpyDeviceToHost) != cudaSuccess) { + cudaFree(device_value); + return 4; + } + + cudaFree(device_value); + std::printf("cuda_smoke_result=%d\n", host_value); + return host_value == 42 ? 0 : 5; +} diff --git a/research/environment/install-r-packages.R b/research/environment/install-r-packages.R new file mode 100644 index 0000000..fc0d3d7 --- /dev/null +++ b/research/environment/install-r-packages.R @@ -0,0 +1,48 @@ +command_args <- commandArgs(trailingOnly = FALSE) +script_path <- sub("^--file=", "", grep("^--file=", command_args, value = TRUE)[1]) +script_dir <- dirname(normalizePath(script_path, winslash = "/")) +project_root <- normalizePath(file.path(script_dir, "..", ".."), winslash = "/") +library_path <- file.path(project_root, ".r-library", "4.6") +dir.create(library_path, recursive = TRUE, showWarnings = FALSE) +.libPaths(c(library_path, .libPaths())) + +required_packages <- c( + "survey", "srvyr", + "mirt", + "lavaan", "semTools", "psych", + "data.table", "jsonlite", "arrow", "haven", "readr", + "dplyr", "tidyr", "purrr", "stringr", + "digest", "renv", "targets", "withr" +) + +installed <- rownames(installed.packages(lib.loc = .libPaths())) +missing <- setdiff(required_packages, installed) + +if (length(missing) > 0L) { + install.packages( + missing, + lib = library_path, + repos = c(CRAN = "https://cloud.r-project.org"), + dependencies = c("Depends", "Imports", "LinkingTo"), + type = "binary", + Ncpus = max(1L, parallel::detectCores(logical = TRUE) - 2L) + ) +} + +installed_after <- rownames(installed.packages(lib.loc = .libPaths())) +still_missing <- setdiff(required_packages, installed_after) +if (length(still_missing) > 0L) { + stop("Missing R packages after installation: ", paste(still_missing, collapse = ", ")) +} + +versions <- vapply( + required_packages, + function(package) as.character(packageVersion(package, lib.loc = library_path)), + character(1) +) +writeLines( + jsonlite::toJSON(as.list(versions), pretty = TRUE, auto_unbox = TRUE), + file.path(script_dir, "r-package-versions.json"), + useBytes = TRUE +) +cat("Installed and verified", length(required_packages), "required R packages.\n") diff --git a/research/environment/python-requirements.in b/research/environment/python-requirements.in new file mode 100644 index 0000000..4d2cbe5 --- /dev/null +++ b/research/environment/python-requirements.in @@ -0,0 +1,40 @@ +# Core data and statistical stack +numpy +scipy +pandas +polars +pyarrow +duckdb +openpyxl +networkx +scikit-learn==1.6.1 +statsmodels +patsy +pyreadstat + +# Bayesian modeling and diagnostics +pymc[nutpie] +numpyro +blackjax +bambi + +# Psychometrics and measurement modeling +semopy +factor-analyzer +pingouin +girth + +# Reproducible analysis and visualization +matplotlib +seaborn +plotly +jupyterlab +ipykernel + +# NLP, embeddings, and tabular baselines +torch==2.14.0 +torchvision==0.29.0 +transformers +sentence-transformers +xgboost +tabpfn diff --git a/research/environment/python-requirements.lock.txt b/research/environment/python-requirements.lock.txt new file mode 100644 index 0000000..78ea87e --- /dev/null +++ b/research/environment/python-requirements.lock.txt @@ -0,0 +1,195 @@ +absl-py==2.5.0 +annotated-doc==0.0.5 +annotated-types==0.8.0 +anyio==4.15.1 +argon2-cffi==25.1.0 +argon2-cffi-bindings==26.1.0 +arro3-core==0.8.3 +arrow==1.4.0 +arviz==1.3.0 +arviz-base==1.3.0 +arviz-plots==1.3.1 +arviz-stats==1.3.2 +asttokens==3.0.2 +async-lru==2.3.0 +attrs==26.1.0 +babel==2.18.0 +bambi==0.21.0 +beautifulsoup4==4.15.0 +blackjax==1.6.2 +bleach==6.4.0 +cachetools==6.2.6 +certifi==2026.7.22 +cffi==2.1.1 +charset-normalizer==3.5.1 +click==8.5.0 +cloudpickle==3.1.2 +colorama==0.4.6 +comm==0.2.3 +contourpy==1.4.0 +cycler==0.12.1 +debugpy==1.8.22 +defusedxml==0.7.1 +donfig==0.8.1.post1 +duckdb==1.5.5 +einops==0.8.2 +et-xmlfile==2.0.0 +executing==2.2.1 +factor-analyzer==0.5.1 +fastjsonschema==2.22.2 +filelock==4.0.1 +fonttools==4.65.0 +formulae==0.6.2 +formulaic==1.2.2 +fqdn==1.5.1 +fsspec==2026.9.0 +girth==0.8.0 +google-crc32c==1.8.0 +graphviz==0.21 +h11==0.16.0 +hf-xet==1.6.0 +httpcore==1.0.9 +httpx==0.28.1 +huggingface-hub==1.32.0 +idna==3.20 +interface-meta==2.0.1 +ipykernel==7.3.0 +ipython==9.17.1 +ipython-pygments-lexers==1.1.1 +isoduration==20.11.0 +jax==0.11.2 +jaxlib==0.11.2 +jedi==0.20.0 +jinja2==3.1.6 +joblib==1.6.0 +json5==0.15.0 +jsonpointer==3.1.1 +jsonschema==4.26.0 +jsonschema-specifications==2025.9.1 +jupyter-builder==1.2.3 +jupyter-client==8.10.0 +jupyter-core==5.9.1 +jupyter-events==0.12.1 +jupyter-lsp==2.3.1 +jupyter-server==2.21.1 +jupyter-server-terminals==0.5.4 +jupyterlab==4.6.3 +jupyterlab-pygments==0.3.0 +jupyterlab-server==2.28.1 +kiwisolver==1.5.1 +lark==1.3.1 +lazy-loader==0.5 +lightgbm==4.7.0 +llvmlite==0.49.0 +markdown-it-py==4.2.0 +markupsafe==3.0.3 +matplotlib==3.11.2 +matplotlib-inline==0.2.2 +mdurl==0.1.2 +mistune==3.3.4 +ml-dtypes==0.6.0 +mpmath==1.3.0 +msgspec==0.21.1 +multipledispatch==1.0.0 +narwhals==2.26.0 +nbclient==0.11.0 +nbconvert==7.17.1 +nbformat==5.11.1 +nest-asyncio2==1.7.2 +networkx==3.6.1 +notebook-shim==0.2.4 +numba==0.67.0 +numcodecs==0.17.0 +numdifftools==0.11.1 +numpy==2.5.3 +numpyro==0.22.0 +nutpie==0.16.11 +obstore==0.11.1 +openpyxl==3.1.5 +opt-einsum==3.4.0 +optax==0.2.8 +packaging==26.3 +pandas==3.0.6 +pandas-flavor==0.8.1 +pandocfilters==1.5.1 +parso==0.8.7 +patsy==1.0.3 +pillow==12.3.0 +pingouin==0.6.1 +pip==26.2.1 +platformdirs==4.11.11 +plotly==7.1.0 +polars==1.44.2 +polars-runtime-32==1.44.2 +prometheus-client==0.26.0 +prompt-toolkit==3.0.53 +psutil==7.2.2 +pure-eval==0.2.4 +pyarrow==25.0.1 +pycparser==3.0 +pydantic==2.13.5 +pydantic-core==2.46.5 +pydantic-settings==2.15.0 +pydot==4.0.1 +pygments==2.21.0 +pymc==6.3.2 +pyparsing==3.3.2 +pyreadstat==1.3.6 +pytensor==3.3.2 +python-dateutil==2.9.0.post0 +python-dotenv==1.2.3 +python-json-logger==4.2.0 +pywinpty==3.0.5 +pyyaml==6.0.3 +pyzmq==27.2.0 +referencing==0.37.0 +regex==2026.9.10 +requests==2.34.2 +rfc3339-validator==0.1.4 +rfc3986-validator==0.1.1 +rfc3987-syntax==1.1.0 +rich==15.0.0 +rpds-py==2026.6.3 +safetensors==0.8.0 +scikit-learn==1.6.1 +scipy==1.18.1 +seaborn==0.13.2 +semopy==2.3.11 +send2trash==2.1.0 +sentence-transformers==6.1.0 +setuptools==84.0.0 +shellingham==1.5.4 +six==1.17.0 +skrub==0.10.1 +soupsieve==2.9.2 +sparse==0.17.0 +stack-data==0.6.3 +statsmodels==0.15.0 +sympy==1.14.0 +tabpfn==9.0.0 +tabulate==0.10.0 +terminado==0.18.1 +threadpoolctl==3.7.0 +tinycss2==1.5.1 +tokenizers==0.23.2 +torch==2.14.0+cu130 +torchvision==0.29.0+cu130 +tornado==6.5.10 +tqdm==4.70.1 +traitlets==5.16.1 +transformers==5.17.0 +typer==0.27.2 +typing-extensions==4.16.0 +typing-inspection==0.4.4 +tzdata==2026.4 +uri-template==1.3.0 +urllib3==2.8.0 +wcwidth==0.8.4 +webcolors==25.10.0 +webencodings==0.6.1 +websocket-client==1.9.2 +wrapt==2.4.1 +xarray==2026.7.0 +xarray-einstats==0.11.0 +xgboost==3.4.1 +zarr==3.4.0 diff --git a/research/environment/r-installed-packages.csv b/research/environment/r-installed-packages.csv new file mode 100644 index 0000000..5de37e7 --- /dev/null +++ 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"survey": "4.5", + "srvyr": "1.3.1", + "mirt": "1.47", + "lavaan": "0.7.2", + "semTools": "0.5.9", + "psych": "2.6.5", + "data.table": "1.18.6.1", + "jsonlite": "2.0.0", + "arrow": "25.0.1", + "haven": "2.5.5", + "readr": "2.2.0", + "dplyr": "1.2.1", + "tidyr": "1.3.2", + "purrr": "1.2.2", + "stringr": "1.6.0", + "digest": "0.6.39", + "renv": "1.2.4", + "targets": "1.12.0", + "withr": "3.0.3" +} diff --git a/research/environment/r-session-info.txt b/research/environment/r-session-info.txt new file mode 100644 index 0000000..51ec818 --- /dev/null +++ b/research/environment/r-session-info.txt @@ -0,0 +1,22 @@ +R version 4.6.1 (2026-06-24 ucrt) +Platform: x86_64-w64-mingw32/x64 +Running under: Windows 11 x64 (build 26100) + +Matrix products: default + LAPACK version 3.12.1 + +locale: +[1] LC_COLLATE=Chinese (Simplified)_China.utf8 +[2] LC_CTYPE=Chinese (Simplified)_China.utf8 +[3] LC_MONETARY=Chinese (Simplified)_China.utf8 +[4] LC_NUMERIC=C +[5] LC_TIME=Chinese (Simplified)_China.utf8 + +time zone: Pacific/Auckland +tzcode source: internal + +attached base packages: +[1] stats graphics grDevices utils datasets methods base + +loaded via a namespace (and not attached): +[1] compiler_4.6.1 tools_4.6.1 renv_1.2.4 diff --git a/research/environment/r-smoke-test-result.json b/research/environment/r-smoke-test-result.json new file mode 100644 index 0000000..13e1968 --- /dev/null +++ b/research/environment/r-smoke-test-result.json @@ -0,0 +1,112 @@ +{ + "r_version": "R version 4.6.1 (2026-06-24 ucrt)", + "library_path": "E:/Model/.r-library/4.6", + "imports": { + "survey": { + "status": "passed", + "version": "4.5" + }, + "srvyr": { + "status": "passed", + "version": "1.3.1" + }, + "mirt": { + "status": "passed", + "version": "1.47" + }, + "lavaan": { + "status": "passed", + "version": "0.7.2" + }, + "semTools": { + "status": "passed", + "version": "0.5.9" + }, + "psych": { + "status": "passed", + "version": "2.6.5" + }, + "data.table": { + "status": "passed", + "version": "1.18.6.1" + }, + "jsonlite": { + "status": "passed", + "version": "2.0.0" + }, + "arrow": { + "status": "passed", + "version": "25.0.1" + }, + "haven": { + "status": "passed", + "version": "2.5.5" + }, + "readr": { + "status": "passed", + "version": "2.2.0" + }, + "dplyr": { + "status": "passed", + "version": "1.2.1" + }, + "tidyr": { + "status": "passed", + "version": "1.3.2" + }, + "purrr": { + "status": "passed", + "version": "1.2.2" + }, + "stringr": { + "status": "passed", + "version": "1.6.0" + }, + "digest": { + "status": "passed", + "version": "0.6.39" + }, + "renv": { + "status": "passed", + "version": "1.2.4" + }, + "targets": { + "status": "passed", + "version": "1.12.0" + }, + "withr": { + "status": "passed", + "version": "3.0.3" + } + }, + "tests": { + "survey_weighted_mean": { + "status": "passed", + "detail": { + "estimate": 3.6667, + "finite": true + } + }, + "lavaan_cfa": { + "status": "passed", + "detail": { + "converged": true + } + }, + "mirt_2pl": { + "status": "passed", + "detail": { + "converged": true, + "items": 8, + "respondents": 500 + } + }, + "arrow_parquet": { + "status": "passed", + "detail": { + "rows": 3, + "columns": 2 + } + } + } +} diff --git a/research/environment/r-smoke-test.R b/research/environment/r-smoke-test.R new file mode 100644 index 0000000..4ef49f9 --- /dev/null +++ b/research/environment/r-smoke-test.R @@ -0,0 +1,113 @@ +command_args <- commandArgs(trailingOnly = FALSE) +script_path <- sub("^--file=", "", grep("^--file=", command_args, value = TRUE)[1]) +script_dir <- dirname(normalizePath(script_path, winslash = "/")) +project_root <- normalizePath(file.path(script_dir, "..", ".."), winslash = "/") +library_path <- file.path(project_root, ".r-library", "4.6") +.libPaths(c(library_path, .libPaths())) + +results <- list( + r_version = R.version.string, + library_path = library_path, + imports = list(), + tests = list() +) + +packages <- c( + "survey", "srvyr", "mirt", "lavaan", "semTools", "psych", + "data.table", "jsonlite", "arrow", "haven", "readr", "dplyr", + "tidyr", "purrr", "stringr", "digest", "renv", "targets", "withr" +) + +for (package in packages) { + status <- tryCatch({ + loadNamespace(package) + list(status = "passed", version = as.character(packageVersion(package))) + }, error = function(error) { + list(status = "failed", detail = conditionMessage(error)) + }) + results$imports[[package]] <- status +} + +run_test <- function(name, expression) { + results$tests[[name]] <<- tryCatch({ + detail <- force(expression) + list(status = "passed", detail = detail) + }, error = function(error) { + list(status = "failed", detail = conditionMessage(error)) + }) +} + +run_test("survey_weighted_mean", { + frame <- data.frame( + value = c(1, 2, 3, 4, 5, 6), + strata = c(1, 1, 1, 2, 2, 2), + psu = c(1, 2, 3, 4, 5, 6), + weight = c(1, 2, 1, 2, 1, 2) + ) + design <- survey::svydesign(~psu, strata = ~strata, weights = ~weight, data = frame) + estimate <- unname(coef(survey::svymean(~value, design))[[1]]) + list(estimate = estimate, finite = is.finite(estimate)) +}) + +run_test("lavaan_cfa", { + set.seed(20260920) + latent <- rnorm(180) + frame <- data.frame( + x1 = 0.8 * latent + rnorm(180, sd = 0.4), + x2 = 0.7 * latent + rnorm(180, sd = 0.5), + x3 = 0.9 * latent + rnorm(180, sd = 0.3) + ) + fit <- lavaan::cfa("factor =~ x1 + x2 + x3", data = frame) + list(converged = lavaan::lavInspect(fit, "converged")) +}) + +run_test("mirt_2pl", { + set.seed(20260920) + sample_size <- 500 + item_count <- 8 + theta <- rnorm(sample_size) + discrimination <- seq(0.8, 1.5, length.out = item_count) + difficulty <- seq(-1.2, 1.2, length.out = item_count) + probabilities <- vapply( + seq_len(item_count), + function(index) plogis(discrimination[index] * (theta - difficulty[index])), + numeric(sample_size) + ) + responses <- matrix( + rbinom(length(probabilities), 1, as.vector(probabilities)), + nrow = sample_size, + ncol = item_count + ) + colnames(responses) <- paste0("item", seq_len(ncol(responses))) + fit <- mirt::mirt( + responses, + 1, + itemtype = "2PL", + verbose = FALSE, + technical = list(NCYCLES = 1000) + ) + converged <- isTRUE(fit@OptimInfo$converged) + if (!converged) stop("mirt 2PL fit did not converge") + list(converged = converged, items = ncol(responses), respondents = nrow(responses)) +}) + +run_test("arrow_parquet", { + cache_dir <- file.path(project_root, ".cache", "r-smoke") + dir.create(cache_dir, recursive = TRUE, showWarnings = FALSE) + path <- file.path(cache_dir, "arrow-smoke.parquet") + arrow::write_parquet(data.frame(id = 1:3, value = c("a", "b", "c")), path) + restored <- arrow::read_parquet(path) + list(rows = nrow(restored), columns = ncol(restored)) +}) + +output_path <- file.path(script_dir, "r-smoke-test-result.json") +writeLines( + jsonlite::toJSON(results, pretty = TRUE, auto_unbox = TRUE, null = "null"), + output_path, + useBytes = TRUE +) +cat(readLines(output_path, warn = FALSE), sep = "\n") + +failed_imports <- names(Filter(function(item) item$status != "passed", results$imports)) +failed_tests <- names(Filter(function(item) item$status != "passed", results$tests)) +quit(status = if (length(failed_imports) || length(failed_tests)) 1L else 0L) diff --git a/research/environment/renv.lock b/research/environment/renv.lock new file mode 100644 index 0000000..f34bb08 --- /dev/null +++ b/research/environment/renv.lock @@ -0,0 +1,4414 @@ +{ + "R": { + "Version": "4.6.1", + "Repositories": [ + { + "Name": "CRAN", + "URL": "https://cloud.r-project.org" + } + ] + }, + "Packages": { + "DBI": { + "Package": "DBI", + "Version": "1.3.0", + "Source": "Repository", + "Title": "R Database Interface", + "Date": "2026-02-11", + "Authors@R": "c( person(\"R Special Interest Group on Databases (R-SIG-DB)\", role = \"aut\"), person(\"Hadley\", \"Wickham\", role = \"aut\"), person(\"Kirill\", \"Müller\", , \"kirill@cynkra.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-1416-3412\")), person(\"R Consortium\", role = \"fnd\") )", + "Description": "A database interface definition for communication between R and relational database management systems. All classes in this package are virtual and need to be extended by the various R/DBMS implementations.", + "License": "LGPL (>= 2.1)", + "URL": "https://dbi.r-dbi.org, https://github.com/r-dbi/DBI", + "BugReports": "https://github.com/r-dbi/DBI/issues", + "Depends": [ + "methods", + "R (>= 3.0.0)" + ], + "Suggests": [ + "arrow", + "blob", + "callr", + "covr", + "DBItest (>= 1.8.2)", + "dbplyr", + "downlit", + "dplyr", + "glue", + "hms", + "knitr", + "magrittr", + "nanoarrow (>= 0.3.0.1)", + "otel", + "otelsdk", + "RMariaDB", + "rmarkdown", + "rprojroot", + "RSQLite (>= 1.1-2)", + "testthat (>= 3.0.0)", + "vctrs", + "xml2" + ], + "VignetteBuilder": "knitr", + "Config/autostyle/scope": "line_breaks", + "Config/autostyle/strict": "false", + "Config/Needs/check": "r-dbi/DBItest", + "Config/Needs/website": "r-dbi/DBItest, r-dbi/dbitemplate, adbi, AzureKusto, bigrquery, DatabaseConnector, dittodb, duckdb, implyr, lazysf, odbc, pool, RAthena, IMSMWU/RClickhouse, RH2, RJDBC, RMariaDB, RMySQL, RPostgres, RPostgreSQL, RPresto, RSQLite, sergeant, sparklyr, withr", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3.9000", + "NeedsCompilation": "no", + "Author": "R Special Interest Group on Databases (R-SIG-DB) [aut], Hadley Wickham [aut], Kirill Müller [aut, cre] (ORCID: ), R Consortium [fnd]", + "Maintainer": "Kirill Müller ", + "Repository": "CRAN" + }, + "Deriv": { + "Package": "Deriv", + "Version": "4.3.5", + "Source": "Repository", + "Type": "Package", + "Title": "Symbolic Differentiation", + "Date": "2026-09-03", + "Authors@R": "c(person(given=\"Andrew\", family=\"Clausen\", role=\"aut\"), person(given=\"Serguei\", family=\"Sokol\", role=c(\"aut\", \"cre\"), email=\"sokol@insa-toulouse.fr\", comment = c(ORCID = \"0000-0002-5674-3327\")), person(given=\"Andreas\", family=\"Rappold\", role=\"ctb\", email=\"arappold@gmx.at\"))", + "Description": "R-based solution for symbolic differentiation. 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Davis [ctb] (ORCID: , SuiteSparse libraries, collaborators listed in dir(system.file(\"doc\", \"SuiteSparse\", package=\"Matrix\"), pattern=\"License\", full.names=TRUE, recursive=TRUE)), George Karypis [ctb] (ORCID: , METIS library, Copyright: Regents of the University of Minnesota), Jason Riedy [ctb] (ORCID: , GNU Octave's condest() and onenormest(), Copyright: Regents of the University of California), Jens Oehlschlägel [ctb] (initial nearPD()), R Core Team [ctb] (ROR: , base R's matrix implementation)", + "Maintainer": "Martin Maechler ", + "Repository": "CRAN" + }, + "R.methodsS3": { + "Package": "R.methodsS3", + "Version": "1.8.2", + "Source": "Repository", + "Depends": [ + "R (>= 2.13.0)" + ], + "Imports": [ + "utils" + ], + "Suggests": [ + "codetools" + ], + "Title": "S3 Methods Simplified", + "Authors@R": "c(person(\"Henrik\", \"Bengtsson\", role=c(\"aut\", \"cre\", \"cph\"), email = \"henrikb@braju.com\"))", + "Author": "Henrik Bengtsson [aut, cre, cph]", + "Maintainer": "Henrik Bengtsson ", + "Description": "Methods that simplify the setup of S3 generic functions and S3 methods. Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers. For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible. The method setMethodS3() is a good start for those who in the future may want to migrate to S4. This is a cross-platform package implemented in pure R that generates standard S3 methods.", + "License": "LGPL (>= 2.1)", + "LazyLoad": "TRUE", + "URL": "https://github.com/HenrikBengtsson/R.methodsS3", + "BugReports": "https://github.com/HenrikBengtsson/R.methodsS3/issues", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "R.oo": { + "Package": "R.oo", + "Version": "1.27.1", + "Source": "Repository", + "Depends": [ + "R (>= 2.13.0)", + "R.methodsS3 (>= 1.8.2)" + ], + "Imports": [ + "methods", + "utils" + ], + "Suggests": [ + "tools" + ], + "Title": "R Object-Oriented Programming with or without References", + "Authors@R": "c(person(\"Henrik\", \"Bengtsson\", role=c(\"aut\", \"cre\", \"cph\"), email = \"henrikb@braju.com\"))", + "Author": "Henrik Bengtsson [aut, cre, cph]", + "Maintainer": "Henrik Bengtsson ", + "Description": "Methods and classes for object-oriented programming in R with or without references. Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers. The package has been developed since 2001 and is now considered very stable. This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.", + "License": "LGPL (>= 2.1)", + "LazyLoad": "TRUE", + "URL": "https://henrikbengtsson.github.io/R.oo/, https://github.com/HenrikBengtsson/R.oo", + "BugReports": "https://github.com/HenrikBengtsson/R.oo/issues", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "R.utils": { + "Package": "R.utils", + "Version": "2.13.0", + "Source": "Repository", + "Depends": [ + "R (>= 2.14.0)", + "R.oo" + ], + "Imports": [ + "methods", + "utils", + "tools", + "R.methodsS3" + ], + "Suggests": [ + "datasets", + "digest (>= 0.6.10)" + ], + "Title": "Various Programming Utilities", + "Authors@R": "c(person(\"Henrik\", \"Bengtsson\", role=c(\"aut\", \"cre\", \"cph\"), email = \"henrikb@braju.com\"))", + "Author": "Henrik Bengtsson [aut, cre, cph]", + "Maintainer": "Henrik Bengtsson ", + "Description": "Utility functions useful when programming and developing R packages.", + "License": "LGPL (>= 2.1)", + "LazyLoad": "TRUE", + "URL": "https://henrikbengtsson.github.io/R.utils/, https://github.com/HenrikBengtsson/R.utils", + "BugReports": "https://github.com/HenrikBengtsson/R.utils/issues", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "R6": { + "Package": "R6", + "Version": "2.6.1", + "Source": "Repository", + "Title": "Encapsulated Classes with Reference Semantics", + "Authors@R": "c( person(\"Winston\", \"Chang\", , \"winston@posit.co\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Creates classes with reference semantics, similar to R's built-in reference classes. Compared to reference classes, R6 classes are simpler and lighter-weight, and they are not built on S4 classes so they do not require the methods package. These classes allow public and private members, and they support inheritance, even when the classes are defined in different packages.", + "License": "MIT + file LICENSE", + "URL": "https://r6.r-lib.org, https://github.com/r-lib/R6", + "BugReports": "https://github.com/r-lib/R6/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Suggests": [ + "lobstr", + "testthat (>= 3.0.0)" + ], + "Config/Needs/website": "tidyverse/tidytemplate, ggplot2, microbenchmark, scales", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Winston Chang [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Winston Chang ", + "Repository": "CRAN" + }, + "Rcpp": { + "Package": "Rcpp", + "Version": "1.1.2", + "Source": "Repository", + "Title": "Seamless R and C++ Integration", + "Date": "2026-07-01", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"JJ\", \"Allaire\", role = \"aut\", comment = c(ORCID = \"0000-0003-0174-9868\")), person(\"Kevin\", \"Ushey\", role = \"aut\", comment = c(ORCID = \"0000-0003-2880-7407\")), person(\"Qiang\", \"Kou\", role = \"aut\", comment = c(ORCID = \"0000-0001-6786-5453\")), person(\"Nathan\", \"Russell\", role = \"aut\"), person(\"Iñaki\", \"Ucar\", role = \"aut\", comment = c(ORCID = \"0000-0001-6403-5550\")), person(\"Doug\", \"Bates\", role = \"aut\", comment = c(ORCID = \"0000-0001-8316-9503\")), person(\"John\", \"Chambers\", role = \"aut\"))", + "Description": "The 'Rcpp' package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about 'Rcpp' is provided by several vignettes included in this package, via the 'Rcpp Gallery' site at , the paper by Eddelbuettel and Francois (2011, ), the book by Eddelbuettel (2013, ) and the paper by Eddelbuettel and Balamuta (2018, ); see 'citation(\"Rcpp\")' for details.", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "methods", + "utils" + ], + "Suggests": [ + "tinytest", + "inline", + "rbenchmark", + "pkgKitten (>= 0.1.2)" + ], + "URL": "https://www.rcpp.org, https://dirk.eddelbuettel.com/code/rcpp.html, https://github.com/RcppCore/Rcpp", + "License": "GPL (>= 2)", + "BugReports": "https://github.com/RcppCore/Rcpp/issues", + "RoxygenNote": "6.1.1", + "Encoding": "UTF-8", + "VignetteBuilder": "Rcpp", + "NeedsCompilation": "yes", + "Author": "Dirk Eddelbuettel [aut, cre] (ORCID: ), Romain Francois [aut] (ORCID: ), JJ Allaire [aut] (ORCID: ), Kevin Ushey [aut] (ORCID: ), Qiang Kou [aut] (ORCID: ), Nathan Russell [aut], Iñaki Ucar [aut] (ORCID: ), Doug Bates [aut] (ORCID: ), John Chambers [aut]", + "Maintainer": "Dirk Eddelbuettel ", + "Repository": "CRAN" + }, + "RcppArmadillo": { + "Package": "RcppArmadillo", + "Version": "15.6.0-1", + "Source": "Repository", + "Type": "Package", + "Title": "'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library", + "Date": "2026-09-07", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Romain\", \"Francois\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Doug\", \"Bates\", role = \"aut\", comment = c(ORCID = \"0000-0001-8316-9503\")), person(\"Binxiang\", \"Ni\", role = \"aut\"), person(\"Conrad\", \"Sanderson\", role = \"aut\", comment = c(ORCID = \"0000-0002-0049-4501\")))", + "Description": "'Armadillo' is a templated C++ linear algebra library aiming towards a good balance between speed and ease of use. It provides high-level syntax and functionality deliberately similar to Matlab. It is useful for algorithm development directly in C++, or quick conversion of research code into production environments. It provides efficient classes for vectors, matrices and cubes where dense and sparse matrices are supported. Integer, floating point and complex numbers are supported. A sophisticated expression evaluator (based on template meta-programming) automatically combines several operations to increase speed and efficiency. Dynamic evaluation automatically chooses optimal code paths based on detected matrix structures. Matrix decompositions are provided through integration with LAPACK, or one of its high performance drop-in replacements (such as 'MKL' or 'OpenBLAS'). It can automatically use 'OpenMP' multi-threading (parallelisation) to speed up computationally expensive operations. . 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For example, the 'parallelFor()' function can be used to convert the work of a standard serial \"for\" loop into a parallel one and the 'parallelReduce()' function can be used for accumulating aggregate or other values.", + "Depends": [ + "R (>= 3.6.0)" + ], + "Suggests": [ + "Rcpp", + "knitr", + "rmarkdown" + ], + "SystemRequirements": "CMake (>= 3.5)", + "License": "GPL (>= 3)", + "URL": "https://rcppcore.github.io/RcppParallel/, https://github.com/RcppCore/RcppParallel", + "BugReports": "https://github.com/RcppCore/RcppParallel/issues", + "Biarch": "TRUE", + "RoxygenNote": "7.3.3", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Kevin Ushey [aut, cre] (ORCID: ), JJ Allaire [aut], Romain Francois [aut, cph], Gregory Vandenbrouck [aut], Marcus Geelnard [aut, cph] (TinyThread library, https://tinythreadpp.bitsnbites.eu/), Hamada S. Badr [ctb] (ORCID: ), Dirk Eddelbuettel [aut] (ORCID: ), Intel [aut, cph] (oneTBB library), UXL Foundation [aut, cph] (oneTBB library), Microsoft [cph], Posit, PBC [cph]", + "Maintainer": "Kevin Ushey ", + "Repository": "CRAN" + }, + "SimDesign": { + "Package": "SimDesign", + "Version": "2.27", + "Source": "Repository", + "Title": "Structure for Organizing Monte Carlo Simulation Designs", + "Authors@R": "c(person(\"Phil\", \"Chalmers\", email = \"rphilip.chalmers@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID=\"0000-0001-5332-2810\")), person(\"Matthew\", \"Sigal\", role = c(\"ctb\")), person(\"Ogreden\", family=\"Oguzhan\", role = c(\"ctb\")), person(\"Mikko\", family=\"Rönkkö\", role = c(\"aut\")), person(\"Moritz\", family=\"Ketzer\", role = c(\"ctb\")))", + "Description": "Provides tools to safely and efficiently organize and execute Monte Carlo simulation experiments in R. The package controls the structure and back-end of Monte Carlo simulation experiments by utilizing a generate-analyse-summarise workflow. The workflow safeguards against common simulation coding issues, such as automatically re-simulating non-convergent results, prevents inadvertently overwriting simulation files, catches error and warning messages during execution, implicitly supports parallel processing with high-quality random number generation, and provides tools for managing high-performance computing (HPC) array jobs submitted to schedulers such as SLURM. For a pedagogical introduction to the package see Sigal and Chalmers (2016) . For a more in-depth overview of the package and its design philosophy see Chalmers and Adkins (2020) .", + "VignetteBuilder": "knitr", + "Depends": [ + "R (>= 4.1.0)" + ], + "Imports": [ + "methods", + "testthat", + "parallel", + "parallelly", + "dplyr", + "sessioninfo", + "beepr", + "pbapply (>= 1.3-0)", + "future", + "future.apply", + "progressr", + "R.utils", + "codetools", + "clipr", + "e1071", + "qs2", + "stats" + ], + "Suggests": [ + "knitr", + "ggplot2", + "tidyr", + "purrr", + "shiny", + "copula", + "extraDistr", + "renv", + "cli", + "job", + "future.batchtools", + "FrF2", + "rmarkdown", + "RPushbullet", + "mirai", + "httr" + ], + "License": "GPL (>= 2)", + "ByteCompile": "yes", + "LazyData": "true", + "URL": "http://philchalmers.github.io/SimDesign/, https://github.com/philchalmers/SimDesign/wiki", + "Encoding": "UTF-8", + "Config/roxygen2/version": "8.1.0", + "NeedsCompilation": "no", + "Author": "Phil Chalmers [aut, cre] (ORCID: ), Matthew Sigal [ctb], Ogreden Oguzhan [ctb], Mikko Rönkkö [aut], Moritz Ketzer [ctb]", + "Maintainer": "Phil Chalmers ", + "Repository": "CRAN" + }, + "arrow": { + "Package": "arrow", + "Version": "25.0.1", + "Source": "Repository", + "Title": "Integration to 'Apache' 'Arrow'", + "Authors@R": "c( person(\"Neal\", \"Richardson\", email = \"neal.p.richardson@gmail.com\", role = c(\"aut\")), person(\"Ian\", \"Cook\", email = \"ianmcook@gmail.com\", role = c(\"aut\")), person(\"Nic\", \"Crane\", email = \"thisisnic@gmail.com\", role = c(\"aut\")), person(\"Dewey\", \"Dunnington\", role = c(\"aut\"), email = \"dewey@fishandwhistle.net\", comment = c(ORCID = \"0000-0002-9415-4582\")), person(\"Romain\", \"Fran\\u00e7ois\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Jonathan\", \"Keane\", email = \"jkeane@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Bryce\", \"Mecum\", email = \"brycemecum@gmail.com\", role = c(\"aut\")), person(\"Drago\\u0219\", \"Moldovan-Gr\\u00fcnfeld\", email = \"dragos.mold@gmail.com\", role = c(\"aut\")), person(\"Jeroen\", \"Ooms\", email = \"jeroen@berkeley.edu\", role = c(\"aut\")), person(\"Jacob\", \"Wujciak-Jens\", email = \"jacob@wujciak.de\", role = c(\"aut\")), person(\"Javier\", \"Luraschi\", email = \"javier@rstudio.com\", role = c(\"ctb\")), person(\"Karl\", \"Dunkle Werner\", email = \"karldw@users.noreply.github.com\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0003-0523-7309\")), person(\"Jeffrey\", \"Wong\", email = \"jeffreyw@netflix.com\", role = c(\"ctb\")), person(\"Apache Arrow\", email = \"dev@arrow.apache.org\", role = c(\"aut\", \"cph\")) )", + "Description": "'Apache' 'Arrow' is a cross-language development platform for in-memory data. 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The backports are conditionally exported in order to let R resolve the function name to either the implemented backport, or the respective base version, if available. Package developers can make use of new functions or arguments by selectively importing specific backports to support older installations.", + "URL": "https://github.com/r-lib/backports", + "BugReports": "https://github.com/r-lib/backports/issues", + "License": "GPL-2 | GPL-3", + "NeedsCompilation": "yes", + "ByteCompile": "yes", + "Depends": [ + "R (>= 3.0.0)" + ], + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "Author": "Michel Lang [cre, aut] (ORCID: ), Duncan Murdoch [aut], R Core Team [aut]", + "Repository": "CRAN" + }, + "base64url": { + "Package": "base64url", + "Version": "1.4", + "Source": "Repository", + "Type": "Package", + "Title": "Fast and URL-Safe Base64 Encoder and Decoder", + "Authors@R": "c( person(\"Michel\", \"Lang\", NULL, \"michellang@gmail.com\", role = c(\"cre\", \"aut\"), comment = c(ORCID = \"0000-0001-9754-0393\")), person(NULL, \"Apache Foundation\", NULL, NULL, role = c(\"ctb\", \"cph\")), person(NULL, \"Free Software Foundation\", NULL, NULL, role = c(\"ctb\", \"cph\")) )", + "Description": "In contrast to RFC3548, the 62nd character (\"+\") is replaced with \"-\", the 63rd character (\"/\") is replaced with \"_\". 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It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready.", + "License": "GPL-3", + "URL": "https://github.com/rasmusab/beepr", + "BugReports": "https://github.com/rasmusab/beepr/issues", + "Imports": [ + "audio" + ], + "RoxygenNote": "7.3.1", + "Suggests": [ + "testthat (>= 3.0.0)" + ], + "Config/testthat/edition": "3", + "NeedsCompilation": "no", + "Author": "Rasmus Bååth [aut, cre], Amanda Dobbyn [ctb]", + "Maintainer": "Rasmus Bååth ", + "Repository": "CRAN" + }, + "bit": { + "Package": "bit", + "Version": "4.6.0", + "Source": "Repository", + "Title": "Classes and Methods for Fast Memory-Efficient Boolean Selections", + "Authors@R": "c( person(\"Michael\", \"Chirico\", email = \"MichaelChirico4@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Jens\", \"Oehlschlägel\", role = \"aut\"), person(\"Brian\", \"Ripley\", role = \"ctb\") )", + "Depends": [ + "R (>= 3.4.0)" + ], + "Suggests": [ + "testthat (>= 3.0.0)", + "roxygen2", + "knitr", + "markdown", + "rmarkdown", + "microbenchmark", + "bit64 (>= 4.0.0)", + "ff (>= 4.0.0)" + ], + "Description": "Provided are classes for boolean and skewed boolean vectors, fast boolean methods, fast unique and non-unique integer sorting, fast set operations on sorted and unsorted sets of integers, and foundations for ff (range index, compression, chunked processing).", + "License": "GPL-2 | GPL-3", + "LazyLoad": "yes", + "ByteCompile": "yes", + "Encoding": "UTF-8", + "URL": "https://github.com/r-lib/bit", + "VignetteBuilder": "knitr, rmarkdown", + "RoxygenNote": "7.3.2", + "Config/testthat/edition": "3", + "NeedsCompilation": "yes", + "Author": "Michael Chirico [aut, cre], Jens Oehlschlägel [aut], Brian Ripley [ctb]", + "Maintainer": "Michael Chirico ", + "Repository": "CRAN" + }, + "bit64": { + "Package": "bit64", + "Version": "4.8.6", + "Source": "Repository", + "Title": "A S3 Class for Vectors of 64bit Integers", + "Authors@R": "c( person(\"Michael\", \"Chirico\", email=\"michaelchirico4@gmail.com\", role=c(\"aut\", \"cre\")), person(\"Jens\", \"Oehlschlägel\", role=\"aut\"), person(\"Leonardo\", \"Silvestri\", role=\"ctb\"), person(\"Ofek\", \"Shilon\", role=\"ctb\"), person(\"Christian\", \"Ullerich\", role=\"ctb\") )", + "Depends": [ + "R (>= 3.5.0)" + ], + "Description": "Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. These are useful for handling database keys and exact counting in +-2^63. WARNING: do not use them as replacement for 32bit integers, integer64 are not supported for subscripting by R-core and they have different semantics when combined with double, e.g. integer64 + double => integer64. Class integer64 can be used in vectors, matrices, arrays and data.frames. Methods are available for coercion from and to logicals, integers, doubles, characters and factors as well as many elementwise and summary functions. Many fast algorithmic operations such as 'match' and 'order' support inter- active data exploration and manipulation and optionally leverage caching.", + "License": "GPL-2 | GPL-3", + "LazyLoad": "yes", + "ByteCompile": "yes", + "URL": "https://github.com/r-lib/bit64, https://bit64.r-lib.org", + "Encoding": "UTF-8", + "Imports": [ + "bit (>= 4.0.0)", + "graphics", + "methods", + "stats", + "utils" + ], + "Suggests": [ + "patrick (>= 0.3.0)", + "testthat (>= 3.3.0)", + "withr" + ], + "Config/testthat/edition": "3", + "Config/Needs/development": "patrick, testthat", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "yes", + "Author": "Michael Chirico [aut, cre], Jens Oehlschlägel [aut], Leonardo Silvestri [ctb], Ofek Shilon [ctb], Christian Ullerich [ctb]", + "Maintainer": "Michael Chirico ", + "Repository": "CRAN" + }, + "boot": { + "Package": "boot", + "Version": "1.3-32", + "Source": "Repository", + "Priority": "recommended", + "Date": "2025-08-29", + "Authors@R": "c(person(\"Angelo\", \"Canty\", role = \"aut\", email = \"cantya@mcmaster.ca\", comment = \"author of original code for S\"), person(\"Brian\", \"Ripley\", role = c(\"aut\", \"trl\"), email = \"Brian.Ripley@R-project.org\", comment = \"conversion to R, maintainer 1999--2022, author of parallel support\"), person(\"Alessandra R.\", \"Brazzale\", role = c(\"ctb\", \"cre\"), email = \"brazzale@stat.unipd.it\", comment = \"minor bug fixes\"))", + "Maintainer": "Alessandra R. Brazzale ", + "Note": "Maintainers are not available to give advice on using a package they did not author.", + "Description": "Functions and datasets for bootstrapping from the book \"Bootstrap Methods and Their Application\" by A. C. Davison and D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.", + "Title": "Bootstrap Functions", + "Depends": [ + "R (>= 3.0.0)", + "graphics", + "stats" + ], + "Suggests": [ + "MASS", + "survival" + ], + "LazyData": "yes", + "ByteCompile": "yes", + "License": "Unlimited", + "NeedsCompilation": "no", + "Author": "Angelo Canty [aut] (author of original code for S), Brian Ripley [aut, trl] (conversion to R, maintainer 1999--2022, author of parallel support), Alessandra R. 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This packages does exactly that.", + "License": "MIT + file LICENSE", + "URL": "https://callr.r-lib.org, https://github.com/r-lib/callr", + "BugReports": "https://github.com/r-lib/callr/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "otel (>= 0.2.0)", + "processx (>= 3.6.1)", + "R6", + "utils" + ], + "Suggests": [ + "asciicast (>= 2.3.1)", + "carrier", + "cli (>= 1.1.0)", + "otelsdk (>= 0.2.0)", + "ps", + "rprojroot", + "spelling", + "testthat (>= 3.2.0)", + "withr (>= 2.3.0)" + ], + "Config/Needs/website": "r-lib/asciicast, glue, htmlwidgets, igraph, tibble, tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-28", + "Encoding": "UTF-8", + "Language": "en-US", + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [aut, cre, cph] (ORCID: ), Winston Chang [aut], Posit Software, PBC [cph, fnd] (ROR: ), Ascent Digital Services [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "class": { + "Package": "class", + "Version": "7.3-23", + "Source": "Repository", + "Priority": "recommended", + "Date": "2025-01-01", + "Depends": [ + "R (>= 3.0.0)", + "stats", + "utils" + ], + "Imports": [ + "MASS" + ], + "Authors@R": "c(person(\"Brian\", \"Ripley\", role = c(\"aut\", \"cre\", \"cph\"), email = \"Brian.Ripley@R-project.org\"), person(\"William\", \"Venables\", role = \"cph\"))", + "Description": "Various functions for classification, including k-nearest neighbour, Learning Vector Quantization and Self-Organizing Maps.", + "Title": "Functions for Classification", + "ByteCompile": "yes", + "License": "GPL-2 | GPL-3", + "URL": "http://www.stats.ox.ac.uk/pub/MASS4/", + "NeedsCompilation": "yes", + "Author": "Brian Ripley [aut, cre, cph], William Venables [cph]", + "Maintainer": "Brian Ripley ", + "Repository": "CRAN" + }, + "cli": { + "Package": "cli", + "Version": "3.6.6", + "Source": "Repository", + "Title": "Helpers for Developing Command Line Interfaces", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"gabor@posit.co\", role = c(\"aut\", \"cre\")), person(\"Hadley\", \"Wickham\", role = \"ctb\"), person(\"Kirill\", \"Müller\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", , \"salim-b@pm.me\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "A suite of tools to build attractive command line interfaces ('CLIs'), from semantic elements: headings, lists, alerts, paragraphs, etc. Supports custom themes via a 'CSS'-like language. It also contains a number of lower level 'CLI' elements: rules, boxes, trees, and 'Unicode' symbols with 'ASCII' alternatives. It support ANSI colors and text styles as well.", + "License": "MIT + file LICENSE", + "URL": "https://cli.r-lib.org, https://github.com/r-lib/cli", + "BugReports": "https://github.com/r-lib/cli/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "utils" + ], + "Suggests": [ + "callr", + "covr", + "crayon", + "digest", + "glue (>= 1.6.0)", + "grDevices", + "htmltools", + "htmlwidgets", + "knitr", + "methods", + "processx", + "ps (>= 1.3.4.9000)", + "rlang (>= 1.0.2.9003)", + "rmarkdown", + "rprojroot", + "rstudioapi", + "testthat (>= 3.2.0)", + "tibble", + "whoami", + "withr" + ], + "Config/Needs/website": "r-lib/asciicast, bench, brio, cpp11, decor, desc, fansi, prettyunits, sessioninfo, tidyverse/tidytemplate, usethis, vctrs", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-25", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2.9000", + "NeedsCompilation": "yes", + "Author": "Gábor Csárdi [aut, cre], Hadley Wickham [ctb], Kirill Müller [ctb], Salim Brüggemann [ctb] (ORCID: ), Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "clipr": { + "Package": "clipr", + "Version": "0.8.1", + "Source": "Repository", + "Type": "Package", + "Title": "Read and Write from the System Clipboard", + "Authors@R": "c( person(\"Matthew\", \"Lincoln\", , \"matthew.d.lincoln@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-4387-3384\")), person(\"Louis\", \"Maddox\", role = \"ctb\"), person(\"Steve\", \"Simpson\", role = \"ctb\"), person(\"Jennifer\", \"Bryan\", role = \"ctb\") )", + "Description": "Simple utility functions to read from and write to the Windows, OS X, and X11 clipboards.", + "License": "GPL-3", + "URL": "https://github.com/mdlincoln/clipr, http://matthewlincoln.net/clipr/", + "BugReports": "https://github.com/mdlincoln/clipr/issues", + "Imports": [ + "utils" + ], + "Suggests": [ + "covr", + "knitr", + "rmarkdown", + "rstudioapi (>= 0.5)", + "testthat (>= 2.0.0)" + ], + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "Language": "en-US", + "RoxygenNote": "7.3.3", + "SystemRequirements": "xclip (https://github.com/astrand/xclip) or xsel (http://www.vergenet.net/~conrad/software/xsel/) for accessing the X11 clipboard, or wl-clipboard (https://github.com/bugaevc/wl-clipboard) for systems using Wayland.", + "NeedsCompilation": "no", + "Author": "Matthew Lincoln [aut, cre] (ORCID: ), Louis Maddox [ctb], Steve Simpson [ctb], Jennifer Bryan [ctb]", + "Maintainer": "Matthew Lincoln ", + "Repository": "CRAN" + }, + "cluster": { + "Package": "cluster", + "Version": "2.1.8.2", + "Source": "Repository", + "VersionNote": "Last CRAN: 2.1.8.1 on 2025-03-11; 2.1.8 on 2024-12-10; 2.1.7 on 2024-12-06; 2.1.6 on 2023-11-30;", + "Date": "2026-02-03", + "Priority": "recommended", + "Title": "\"Finding Groups in Data\": Cluster Analysis Extended Rousseeuw et al.", + "Description": "Methods for Cluster analysis. Much extended the original from Peter Rousseeuw, Anja Struyf and Mia Hubert, based on Kaufman and Rousseeuw (1990) \"Finding Groups in Data\".", + "Maintainer": "Martin Maechler ", + "Authors@R": "c(person(\"Martin\",\"Maechler\", role = c(\"aut\",\"cre\"), email=\"maechler@stat.math.ethz.ch\", comment = c(ORCID = \"0000-0002-8685-9910\")) ,person(\"Peter\", \"Rousseeuw\", role=\"aut\", email=\"peter.rousseeuw@kuleuven.be\", comment = c(\"Fortran original\", ORCID = \"0000-0002-3807-5353\")) ,person(\"Anja\", \"Struyf\", role=\"aut\", comment= \"S original\") ,person(\"Mia\", \"Hubert\", role=\"aut\", email= \"Mia.Hubert@uia.ua.ac.be\", comment = c(\"S original\", ORCID = \"0000-0001-6398-4850\")) ,person(\"Kurt\", \"Hornik\", role=c(\"trl\", \"ctb\"), email=\"Kurt.Hornik@R-project.org\", comment=c(\"port to R; maintenance(1999-2000)\", ORCID=\"0000-0003-4198-9911\")) ,person(\"Matthias\", \"Studer\", role=\"ctb\") ,person(\"Pierre\", \"Roudier\", role=\"ctb\") ,person(\"Juan\", \"Gonzalez\", role=\"ctb\") ,person(\"Kamil\", \"Kozlowski\", role=\"ctb\") ,person(\"Erich\", \"Schubert\", role=\"ctb\", comment = c(\"fastpam options for pam()\", ORCID = \"0000-0001-9143-4880\")) ,person(\"Keefe\", \"Murphy\", role=\"ctb\", comment = \"volume.ellipsoid({d >= 3})\") #not yet ,person(\"Fischer-Rasmussen\", \"Kasper\", role = \"ctb\", comment = \"Gower distance for CLARA\") )", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "graphics", + "grDevices", + "stats", + "utils" + ], + "Suggests": [ + "MASS", + "Matrix" + ], + "SuggestsNote": "MASS: two examples using cov.rob() and mvrnorm(); Matrix tools for testing", + "Enhances": [ + "mvoutlier", + "fpc", + "ellipse", + "sfsmisc" + ], + "EnhancesNote": "xref-ed in man/*.Rd", + "LazyLoad": "yes", + "LazyData": "yes", + "ByteCompile": "yes", + "BuildResaveData": "no", + "License": "GPL (>= 2)", + "URL": "https://svn.r-project.org/R-packages/trunk/cluster/", + "NeedsCompilation": "yes", + "Author": "Martin Maechler [aut, cre] (ORCID: ), Peter Rousseeuw [aut] (Fortran original, ORCID: ), Anja Struyf [aut] (S original), Mia Hubert [aut] (S original, ORCID: ), Kurt Hornik [trl, ctb] (port to R; maintenance(1999-2000), ORCID: ), Matthias Studer [ctb], Pierre Roudier [ctb], Juan Gonzalez [ctb], Kamil Kozlowski [ctb], Erich Schubert [ctb] (fastpam options for pam(), ORCID: ), Keefe Murphy [ctb] (volume.ellipsoid({d >= 3}))", + "Repository": "CRAN" + }, + "codetools": { + "Package": "codetools", + "Version": "0.2-20", + "Source": "Repository", + "Priority": "recommended", + "Author": "Luke Tierney ", + "Description": "Code analysis tools for R.", + "Title": "Code Analysis Tools for R", + "Depends": [ + "R (>= 2.1)" + ], + "Maintainer": "Luke Tierney ", + "URL": "https://gitlab.com/luke-tierney/codetools", + "License": "GPL", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "cpp11": { + "Package": "cpp11", + "Version": "0.5.5", + "Source": "Repository", + "Title": "A C++11 Interface for R's C Interface", + "Authors@R": "c( person(\"Davis\", \"Vaughan\", email = \"davis@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4777-038X\")), person(\"Jim\",\"Hester\", role = \"aut\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Romain\", \"François\", role = \"aut\", comment = c(ORCID = \"0000-0002-2444-4226\")), person(\"Benjamin\", \"Kietzman\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.", + "License": "MIT + file LICENSE", + "URL": "https://cpp11.r-lib.org, https://github.com/r-lib/cpp11", + "BugReports": "https://github.com/r-lib/cpp11/issues", + "Depends": [ + "R (>= 4.0.0)" + ], + "Suggests": [ + "bench", + "brio", + "callr", + "cli", + "covr", + "decor", + "desc", + "ggplot2", + "glue", + "knitr", + "lobstr", + "mockery", + "progress", + "rmarkdown", + "scales", + "Rcpp", + "testthat (>= 3.2.0)", + "tibble", + "utils", + "vctrs", + "withr" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/Needs/cpp11/cpp_register": "brio, cli, decor, desc, glue, tibble, vctrs", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "no", + "Author": "Davis Vaughan [aut, cre] (ORCID: ), Jim Hester [aut] (ORCID: ), Romain François [aut] (ORCID: ), Benjamin Kietzman [ctb], Posit Software, PBC [cph, fnd]", + "Maintainer": "Davis Vaughan ", + "Repository": "CRAN" + }, + "crayon": { + "Package": "crayon", + "Version": "1.5.3", + "Source": "Repository", + "Title": "Colored Terminal Output", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Brodie\", \"Gaslam\", , \"brodie.gaslam@yahoo.com\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "The crayon package is now superseded. Please use the 'cli' package for new projects. Colored terminal output on terminals that support 'ANSI' color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI' color support is automatically detected. Colors and highlighting can be combined and nested. New styles can also be created easily. This package was inspired by the 'chalk' 'JavaScript' project.", + "License": "MIT + file LICENSE", + "URL": "https://r-lib.github.io/crayon/, https://github.com/r-lib/crayon", + "BugReports": "https://github.com/r-lib/crayon/issues", + "Imports": [ + "grDevices", + "methods", + "utils" + ], + "Suggests": [ + "mockery", + "rstudioapi", + "testthat", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.1", + "Collate": "'aaa-rstudio-detect.R' 'aaaa-rematch2.R' 'aab-num-ansi-colors.R' 'aac-num-ansi-colors.R' 'ansi-256.R' 'ansi-palette.R' 'combine.R' 'string.R' 'utils.R' 'crayon-package.R' 'disposable.R' 'enc-utils.R' 'has_ansi.R' 'has_color.R' 'link.R' 'styles.R' 'machinery.R' 'parts.R' 'print.R' 'style-var.R' 'show.R' 'string_operations.R'", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [aut, cre], Brodie Gaslam [ctb], Posit Software, PBC [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "data.table": { + "Package": "data.table", + "Version": "1.18.6.1", + "Source": "Repository", + "Title": "Extension of `data.frame`", + "Depends": [ + "R (>= 3.4.0)" + ], + "Imports": [ + "methods" + ], + "Suggests": [ + "bit64 (>= 4.0.0)", + "bit (>= 4.0.4)", + "R.utils (>= 2.13.0)", + "xts", + "zoo (>= 1.8-1)", + "yaml", + "knitr", + "markdown" + ], + "Description": "Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.", + "License": "MPL-2.0 | file LICENSE", + "URL": "https://r-datatable.com, https://Rdatatable.gitlab.io/data.table, https://github.com/Rdatatable/data.table", + "BugReports": "https://github.com/Rdatatable/data.table/issues", + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "ByteCompile": "TRUE", + "Authors@R": "c( person(\"Tyson\",\"Barrett\", role=c(\"aut\",\"cre\"), email=\"t.barrett88@gmail.com\", comment = c(ORCID=\"0000-0002-2137-1391\")), person(\"Matt\",\"Dowle\", role=\"aut\", email=\"mattjdowle@gmail.com\"), person(\"Arun\",\"Srinivasan\", role=\"aut\", email=\"asrini@pm.me\"), person(\"Jan\",\"Gorecki\", role=\"aut\", email=\"j.gorecki@wit.edu.pl\"), person(\"Michael\",\"Chirico\", role=\"aut\", email=\"michaelchirico4@gmail.com\", comment = c(ORCID=\"0000-0003-0787-087X\")), person(\"Toby\",\"Hocking\", role=\"aut\", email=\"toby.hocking@r-project.org\", comment = c(ORCID=\"0000-0002-3146-0865\")), 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role=\"ctb\"), person(\"Bill\", \"Evans\", role=\"ctb\"), person(\"Reino\", \"Bruner\", role=\"ctb\"), person(given=\"@badasahog\", role=\"ctb\", comment=\"GitHub user\"), person(\"Vinit\", \"Thakur\", role=\"ctb\"), person(\"Mukul\", \"Kumar\", role=\"ctb\"), person(\"Ildikó\", \"Czeller\", role=\"ctb\"), person(\"Manmita\", \"Das\", role=\"ctb\"), person(\"Tarun\", \"Thammisetty\", role=\"ctb\"), person(\"Marco\", \"Colombo\", role=\"ctb\", comment = c(ORCID = \"0000-0001-6672-0623\")), person(\"Tim\", \"Taylor\", role=\"ctb\", comment = c(ORCID = \"0000-0002-8587-7113\")) )", + "NeedsCompilation": "yes", + "Author": "Tyson Barrett [aut, cre] (ORCID: ), Matt Dowle [aut], Arun Srinivasan [aut], Jan Gorecki [aut], Michael Chirico [aut] (ORCID: ), Toby Hocking [aut] (ORCID: ), Benjamin Schwendinger [aut] (ORCID: ), Ivan Krylov [aut] (ORCID: ), Pasha Stetsenko [ctb], Tom Short [ctb], Steve Lianoglou [ctb], Eduard Antonyan [ctb], Markus Bonsch [ctb], Hugh Parsonage [ctb], Scott Ritchie [ctb], Kun Ren [ctb], Xianying Tan [ctb], Rick Saporta [ctb], Otto Seiskari [ctb], Xianghui Dong [ctb], Michel Lang [ctb], Watal Iwasaki [ctb], Seth Wenchel [ctb], Karl Broman [ctb], Tobias Schmidt [ctb], David Arenburg [ctb], Ethan Smith [ctb], Francois Cocquemas [ctb], Matthieu Gomez [ctb], Philippe Chataignon [ctb], Nello Blaser [ctb], Dmitry Selivanov [ctb], Andrey Riabushenko [ctb], Cheng Lee [ctb], Declan Groves [ctb], Daniel Possenriede [ctb], Felipe Parages [ctb], Denes Toth [ctb], Mus Yaramaz-David [ctb], Ayappan Perumal [ctb], James Sams [ctb], Martin Morgan [ctb], Michael Quinn [ctb], @javrucebo [ctb] (GitHub user), Marc Halperin [ctb], Roy Storey [ctb], Manish Saraswat [ctb], Morgan Jacob [ctb], Michael Schubmehl [ctb], Davis Vaughan [ctb], Leonardo Silvestri [ctb], Jim Hester [ctb], Anthony Damico [ctb], Sebastian Freundt [ctb], David Simons [ctb], Elliott Sales de Andrade [ctb], Cole Miller [ctb], Jens Peder Meldgaard [ctb], Vaclav Tlapak [ctb], Kevin Ushey [ctb], Dirk Eddelbuettel [ctb], Tony Fischetti [ctb], Ofek Shilon [ctb], Vadim Khotilovich [ctb], Hadley Wickham [ctb], Bennet Becker [ctb], Kyle Haynes [ctb], Boniface Christian Kamgang [ctb], Olivier Delmarcell [ctb], Josh O'Brien [ctb], Dereck Mezquita [ctb] (ORCID: ), Michael Czekanski [ctb], Dmitry Shemetov [ctb], Nitish Jha [ctb], Joshua Wu [ctb], Iago Giné-Vázquez [ctb], Anirban Chetia [ctb], Doris Amoakohene [ctb], Angel Feliz [ctb], Michael Young [ctb], Mark Seeto [ctb], Philippe Grosjean [ctb], Vincent Runge [ctb], Christian Wia [ctb], Elise Maigné [ctb], Vincent Rocher [ctb], Vijay Lulla [ctb], Aljaž Sluga [ctb], Bill Evans [ctb], Reino Bruner [ctb], @badasahog [ctb] (GitHub user), Vinit Thakur [ctb], Mukul Kumar [ctb], Ildikó Czeller [ctb], Manmita Das [ctb], Tarun Thammisetty [ctb], Marco Colombo [ctb] (ORCID: ), Tim Taylor [ctb] (ORCID: )", + "Maintainer": "Tyson Barrett ", + "Repository": "CRAN" + }, + "dcurver": { + "Package": "dcurver", + "Version": "0.9.3", + "Source": "Repository", + "Date": "2025-10-24", + "Title": "Utility Functions for Davidian Curves", + "Authors@R": "person(given = \"Ozan\", family = \"Öğreden\", role = c(\"aut\", \"cre\"), email = \"oguzhanogreden@protonmail.com\")", + "Description": "A Davidian curve defines a seminonparametric density, whose shape and flexibility can be tuned by easy to estimate parameters. Since a special case of a Davidian curve is the standard normal density, Davidian curves can be used for relaxing normality assumption in statistical applications (Zhang & Davidian, 2001) . This package provides the density function, the gradient of the loglikelihood and a random generator for Davidian curves.", + "License": "GPL-3", + "URL": "https://github.com/oguzhanogreden/dcurver", + "BugReports": "https://github.com/oguzhanogreden/dcurver/issues", + "Imports": [ + "Rcpp (>= 0.12.14)" + ], + "LinkingTo": [ + "Rcpp", + "RcppArmadillo" + ], + "RoxygenNote": "7.3.2", + "Encoding": "UTF-8", + "Suggests": [ + "testthat" + ], + "Language": "en-US", + "NeedsCompilation": "yes", + "Author": "Ozan Öğreden [aut, cre]", + "Maintainer": "Ozan Öğreden ", + "Repository": "CRAN" + }, + "desc": { + "Package": "desc", + "Version": "1.4.3", + "Source": "Repository", + "Title": "Manipulate DESCRIPTION Files", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Kirill\", \"Müller\", role = \"aut\"), person(\"Jim\", \"Hester\", , \"james.f.hester@gmail.com\", role = \"aut\"), person(\"Maëlle\", \"Salmon\", role = \"ctb\", comment = c(ORCID = \"0000-0002-2815-0399\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Maintainer": "Gábor Csárdi ", + "Description": "Tools to read, write, create, and manipulate DESCRIPTION files. It is intended for packages that create or manipulate other packages.", + "License": "MIT + file LICENSE", + "URL": "https://desc.r-lib.org/, https://github.com/r-lib/desc", + "BugReports": "https://github.com/r-lib/desc/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "cli", + "R6", + "utils" + ], + "Suggests": [ + "callr", + "covr", + "gh", + "spelling", + "testthat", + "whoami", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "Language": "en-US", + "RoxygenNote": "7.2.3", + "Collate": "'assertions.R' 'authors-at-r.R' 'built.R' 'classes.R' 'collate.R' 'constants.R' 'deps.R' 'desc-package.R' 'description.R' 'encoding.R' 'find-package-root.R' 'latex.R' 'non-oo-api.R' 'package-archives.R' 'read.R' 'remotes.R' 'str.R' 'syntax_checks.R' 'urls.R' 'utils.R' 'validate.R' 'version.R'", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [aut, cre], Kirill Müller [aut], Jim Hester [aut], Maëlle Salmon [ctb] (), Posit Software, PBC [cph, fnd]", + "Repository": "CRAN" + }, + "diffobj": { + "Package": "diffobj", + "Version": "0.3.9", + "Source": "Repository", + "Type": "Package", + "Title": "Diffs for R Objects", + "Description": "Generate a colorized diff of two R objects for an intuitive visualization of their differences.", + "Authors@R": "c( person( \"Brodie\", \"Gaslam\", email=\"brodie.gaslam@yahoo.com\", role=c(\"aut\", \"cre\")), person( \"Michael B.\", \"Allen\", email=\"ioplex@gmail.com\", role=c(\"ctb\", \"cph\"), comment=\"Original C implementation of Myers Diff Algorithm\"))", + "Depends": [ + "R (>= 4.1.0)" + ], + "License": "GPL-2 | GPL-3", + "URL": "https://github.com/brodieG/diffobj", + "BugReports": "https://github.com/brodieG/diffobj/issues", + "VignetteBuilder": "knitr", + "Encoding": "UTF-8", + "Suggests": [ + "knitr", + "rmarkdown" + ], + "Collate": "'capt.R' 'options.R' 'pager.R' 'check.R' 'finalizer.R' 'misc.R' 'html.R' 'styles.R' 's4.R' 'core.R' 'diff.R' 'get.R' 'guides.R' 'hunks.R' 'layout.R' 'myerssimple.R' 'rdiff.R' 'rds.R' 'set.R' 'subset.R' 'summmary.R' 'system.R' 'text.R' 'tochar.R' 'trim.R' 'word.R'", + "Imports": [ + "crayon (>= 1.3.2)", + "tools", + "methods", + "utils", + "stats" + ], + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "yes", + "Author": "Brodie Gaslam [aut, cre], Michael B. Allen [ctb, cph] (Original C implementation of Myers Diff Algorithm)", + "Maintainer": "Brodie Gaslam ", + "Repository": "CRAN" + }, + "digest": { + "Package": "digest", + "Version": "0.6.39", + "Source": "Repository", + "Authors@R": "c(person(\"Dirk\", \"Eddelbuettel\", role = c(\"aut\", \"cre\"), email = \"edd@debian.org\", comment = c(ORCID = \"0000-0001-6419-907X\")), person(\"Antoine\", \"Lucas\", role=\"ctb\", comment = c(ORCID = \"0000-0002-8059-9767\")), person(\"Jarek\", \"Tuszynski\", role=\"ctb\"), person(\"Henrik\", \"Bengtsson\", role=\"ctb\", comment = c(ORCID = \"0000-0002-7579-5165\")), person(\"Simon\", \"Urbanek\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2297-1732\")), person(\"Mario\", \"Frasca\", role=\"ctb\"), person(\"Bryan\", \"Lewis\", role=\"ctb\"), person(\"Murray\", \"Stokely\", role=\"ctb\"), person(\"Hannes\", \"Muehleisen\", role=\"ctb\", comment = c(ORCID = \"0000-0001-8552-0029\")), person(\"Duncan\", \"Murdoch\", role=\"ctb\"), person(\"Jim\", \"Hester\", role=\"ctb\", comment = c(ORCID = \"0000-0002-2739-7082\")), person(\"Wush\", \"Wu\", role=\"ctb\", comment = c(ORCID = \"0000-0001-5180-0567\")), person(\"Qiang\", \"Kou\", role=\"ctb\", comment = c(ORCID = \"0000-0001-6786-5453\")), person(\"Thierry\", \"Onkelinx\", role=\"ctb\", comment = c(ORCID = \"0000-0001-8804-4216\")), person(\"Michel\", \"Lang\", role=\"ctb\", comment = c(ORCID = \"0000-0001-9754-0393\")), person(\"Viliam\", \"Simko\", role=\"ctb\"), person(\"Kurt\", \"Hornik\", role=\"ctb\", comment = c(ORCID = \"0000-0003-4198-9911\")), person(\"Radford\", \"Neal\", role=\"ctb\", comment = c(ORCID = \"0000-0002-2473-3407\")), person(\"Kendon\", \"Bell\", role=\"ctb\", comment = c(ORCID = \"0000-0002-9093-8312\")), person(\"Matthew\", \"de Queljoe\", role=\"ctb\"), person(\"Dmitry\", \"Selivanov\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0492-6647\")), person(\"Ion\", \"Suruceanu\", role=\"ctb\", comment = c(ORCID = \"0009-0005-6446-4909\")), person(\"Bill\", \"Denney\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5759-428X\")), person(\"Dirk\", \"Schumacher\", role=\"ctb\"), person(\"András\", \"Svraka\", role=\"ctb\", comment = c(ORCID = \"0009-0008-8480-1329\")), person(\"Sergey\", \"Fedorov\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5970-7233\")), person(\"Will\", \"Landau\", role=\"ctb\", comment = c(ORCID = \"0000-0003-1878-3253\")), person(\"Floris\", \"Vanderhaeghe\", role=\"ctb\", comment = c(ORCID = \"0000-0002-6378-6229\")), person(\"Kevin\", \"Tappe\", role=\"ctb\"), person(\"Harris\", \"McGehee\", role=\"ctb\"), person(\"Tim\", \"Mastny\", role=\"ctb\"), person(\"Aaron\", \"Peikert\", role=\"ctb\", comment = c(ORCID = \"0000-0001-7813-818X\")), person(\"Mark\", \"van der Loo\", role=\"ctb\", comment = c(ORCID = \"0000-0002-9807-4686\")), person(\"Chris\", \"Muir\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2555-3878\")), person(\"Moritz\", \"Beller\", role=\"ctb\", comment = c(ORCID = \"0000-0003-4852-0526\")), person(\"Sebastian\", \"Campbell\", role=\"ctb\", comment = c(ORCID = \"0009-0000-5948-4503\")), person(\"Winston\", \"Chang\", role=\"ctb\", comment = c(ORCID = \"0000-0002-1576-2126\")), person(\"Dean\", \"Attali\", role=\"ctb\", comment = c(ORCID = \"0000-0002-5645-3493\")), person(\"Michael\", \"Chirico\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0787-087X\")), person(\"Kevin\", \"Ushey\", role=\"ctb\", comment = c(ORCID = \"0000-0003-2880-7407\")), person(\"Carl\", \"Pearson\", role=\"ctb\", comment = c(ORCID = \"0000-0003-0701-7860\")))", + "Date": "2025-11-19", + "Title": "Create Compact Hash Digests of R Objects", + "Description": "Implementation of a function 'digest()' for the creation of hash digests of arbitrary R objects (using the 'md5', 'sha-1', 'sha-256', 'crc32', 'xxhash', 'murmurhash', 'spookyhash', 'blake3', 'crc32c', 'xxh3_64', and 'xxh3_128' algorithms) permitting easy comparison of R language objects, as well as functions such as 'hmac()' to create hash-based message authentication code. Please note that this package is not meant to be deployed for cryptographic purposes for which more comprehensive (and widely tested) libraries such as 'OpenSSL' should be used.", + "URL": "https://github.com/eddelbuettel/digest, https://eddelbuettel.github.io/digest/, https://dirk.eddelbuettel.com/code/digest.html", + "BugReports": "https://github.com/eddelbuettel/digest/issues", + "Depends": [ + "R (>= 3.3.0)" + ], + "Imports": [ + "utils" + ], + "License": "GPL (>= 2)", + "Suggests": [ + "tinytest", + "simplermarkdown", + "rbenchmark" + ], + "VignetteBuilder": "simplermarkdown", + "Encoding": "UTF-8", + "NeedsCompilation": "yes", + "Author": "Dirk Eddelbuettel [aut, cre] (ORCID: ), Antoine Lucas [ctb] (ORCID: ), Jarek Tuszynski [ctb], Henrik Bengtsson [ctb] (ORCID: ), Simon Urbanek [ctb] (ORCID: ), Mario Frasca [ctb], Bryan Lewis [ctb], Murray Stokely [ctb], Hannes Muehleisen [ctb] (ORCID: ), Duncan Murdoch [ctb], Jim Hester [ctb] (ORCID: ), Wush Wu [ctb] (ORCID: ), Qiang Kou [ctb] 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The function rst2pdf() requires rst2pdf (https://github.com/rst2pdf/rst2pdf).", + "Collate": "'block.R' 'cache.R' 'citation.R' 'hooks-html.R' 'plot.R' 'utils.R' 'defaults.R' 'concordance.R' 'engine.R' 'highlight.R' 'themes.R' 'header.R' 'hooks-asciidoc.R' 'hooks-chunk.R' 'hooks-extra.R' 'hooks-latex.R' 'hooks-md.R' 'hooks-rst.R' 'hooks-textile.R' 'hooks-typst.R' 'hooks.R' 'otel.R' 'output.R' 'package.R' 'pandoc.R' 'params.R' 'parser.R' 'pattern.R' 'rocco.R' 'spin.R' 'table.R' 'template.R' 'utils-conversion.R' 'utils-rd2html.R' 'utils-string.R' 'utils-sweave.R' 'utils-upload.R' 'utils-vignettes.R' 'zzz.R'", + "Config/roxygen2/version": "8.1.0", + "NeedsCompilation": "no", + "Author": "Yihui Xie [aut, cre] (ORCID: , URL: https://yihui.org), Abhraneel Sarma [ctb], Adam Vogt [ctb], Alastair Andrew [ctb], Alex Zvoleff [ctb], Amar Al-Zubaidi [ctb], Andre Simon [ctb] (the CSS files under inst/themes/ were derived from the Highlight package http://www.andre-simon.de), Aron Atkins [ctb], Aaron Wolen [ctb], Ashley Manton [ctb], Atsushi Yasumoto [ctb] (ORCID: ), Ben Baumer [ctb], Brian Diggs [ctb], Brian Zhang [ctb], Bulat Yapparov [ctb], Cassio Pereira [ctb], Christophe Dervieux [ctb], David Hall [ctb], David Hugh-Jones [ctb], David Robinson [ctb], Doug Hemken [ctb], Duncan Murdoch [ctb], Elio Campitelli [ctb], Ellis Hughes [ctb], Emily Riederer [ctb], Fabian Hirschmann [ctb], Fitch Simeon [ctb], Forest Fang [ctb], Frank E Harrell Jr [ctb] (the Sweavel package at inst/misc/Sweavel.sty), Garrick Aden-Buie [ctb], Gregoire Detrez [ctb], Hadley Wickham [ctb], Hao Zhu [ctb], Heewon Jeon [ctb], Henrik Bengtsson [ctb], Hiroaki Yutani [ctb], Ian Lyttle [ctb], Hodges Daniel [ctb], Jacob Bien [ctb], Jake Burkhead [ctb], James Manton [ctb], Jared Lander [ctb], Jason Punyon [ctb], Javier Luraschi [ctb], Jeff Arnold [ctb], Jenny Bryan [ctb], Jeremy Ashkenas [ctb, cph] (the CSS file at inst/misc/docco-classic.css), Jeremy Stephens [ctb], Jim Hester [ctb], Joe Cheng [ctb], Johan Larsson [ctb], Johannes Ranke [ctb], John Honaker [ctb], John Muschelli [ctb], Jonathan Keane [ctb], JJ Allaire [ctb], Johan Toloe [ctb], Jonathan Sidi [ctb], Joseph Larmarange [ctb], Julien Barnier [ctb], Kaiyin Zhong [ctb], Kamil Slowikowski [ctb], Karl Forner [ctb], Kevin K. 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Kamvar [ctb] (ORCID: ), Posit Software, PBC [cph, fnd]", + "Maintainer": "Yihui Xie ", + "Repository": "CRAN" + }, + "lattice": { + "Package": "lattice", + "Version": "0.22-9", + "Source": "Repository", + "Date": "2026-02-03", + "Priority": "recommended", + "Title": "Trellis Graphics for R", + "Authors@R": "c(person(\"Deepayan\", \"Sarkar\", role = c(\"aut\", \"cre\"), email = \"deepayan.sarkar@r-project.org\", comment = c(ORCID = \"0000-0003-4107-1553\")), person(\"Felix\", \"Andrews\", role = \"ctb\"), person(\"Kevin\", \"Wright\", role = \"ctb\", comment = \"documentation\"), person(\"Neil\", \"Klepeis\", role = \"ctb\"), person(\"Johan\", \"Larsson\", role = \"ctb\", comment = \"miscellaneous improvements\"), person(\"Zhijian (Jason)\", \"Wen\", role = \"cph\", comment = \"filled contour code\"), person(\"Paul\", \"Murrell\", role = \"ctb\", email = \"paul@stat.auckland.ac.nz\"), person(\"Stefan\", \"Eng\", role = \"ctb\", comment = \"violin plot improvements\"), person(\"Achim\", \"Zeileis\", role = \"ctb\", comment = \"modern colors\"), person(\"Alexandre\", \"Courtiol\", role = \"ctb\", comment = \"generics for larrows, lpolygon, lrect and lsegments\") )", + "Description": "A powerful and elegant high-level data visualization system inspired by Trellis graphics, with an emphasis on multivariate data. 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This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions. For more information, see package vignette. To quote Rene Magritte, \"Ceci n'est pas un pipe.\"", + "License": "MIT + file LICENSE", + "URL": "https://magrittr.tidyverse.org, https://github.com/tidyverse/magrittr", + "BugReports": "https://github.com/tidyverse/magrittr/issues", + "Depends": [ + "R (>= 3.4.0)" + ], + "Suggests": [ + "covr", + "knitr", + "rlang", + "rmarkdown", + "testthat" + ], + "VignetteBuilder": "knitr", + "ByteCompile": "Yes", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Stefan Milton Bache [aut, cph] (Original author and creator of magrittr), Hadley Wickham [aut], Lionel Henry [cre], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Lionel Henry ", + "Repository": "CRAN" + }, + "mgcv": { + "Package": "mgcv", + "Version": "1.9-4", + "Source": "Repository", + "Authors@R": "person(given = \"Simon\", family = \"Wood\", role = c(\"aut\", \"cre\"), email = \"simon.wood@r-project.org\")", + "Title": "Mixed GAM Computation Vehicle with Automatic Smoothness Estimation", + "Description": "Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2025) for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.", + "Priority": "recommended", + "Depends": [ + "R (>= 4.4.0)", + "nlme (>= 3.1-64)" + ], + "Imports": [ + "methods", + "stats", + "graphics", + "Matrix", + "splines", + "utils" + ], + "Suggests": [ + "parallel", + "survival", + "MASS" + ], + "LazyLoad": "yes", + "ByteCompile": "yes", + "License": "GPL (>= 2)", + "NeedsCompilation": "yes", + "Author": "Simon Wood [aut, cre]", + "Maintainer": "Simon Wood ", + "Repository": "CRAN" + }, + "minqa": { + "Package": "minqa", + "Version": "1.2.8", + "Source": "Repository", + "Type": "Package", + "Title": "Derivative-Free Optimization Algorithms by Quadratic Approximation", + "Authors@R": "c(person(given = \"Douglas\", family = \"Bates\", role = \"aut\"), person(given = c(\"Katharine\", \"M.\"), family = \"Mullen\", role = c(\"aut\", \"cre\"), email = \"katharine.mullen@stat.ucla.edu\"), person(given = c(\"John\", \"C.\"), family = \"Nash\", role = \"aut\"), person(given = \"Ravi\", family = \"Varadhan\", role = \"aut\"))", + "Maintainer": "Katharine M. 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Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. 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The implementation can easily be added to functions where showing the progress is useful (e.g. bootstrap). The type and style of the progress bar (with percentages or remaining time) can be set through options. Supports several parallel processing backends including mirai and future.", + "Depends": [ + "R (>= 3.2.0)" + ], + "Imports": [ + "parallel" + ], + "Suggests": [ + "shiny", + "future", + "future.apply" + ], + "License": "GPL (>= 2)", + "URL": "https://github.com/psolymos/pbapply, https://peter.solymos.org/pbapply/", + "BugReports": "https://github.com/psolymos/pbapply/issues", + "NeedsCompilation": "no", + "Author": "Peter Solymos [aut, cre] (ORCID: ), Zygmunt Zawadzki [aut], Henrik Bengtsson [ctb], R Core Team [cph, ctb]", + "Repository": "CRAN" + }, + "pbivnorm": { + "Package": "pbivnorm", + "Version": "0.6.0", + "Source": "Repository", + "Title": "Vectorized Bivariate Normal CDF", + "Date": "2015-01-23", + "Author": "Fortran code by Alan Genz. 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This is a key part of the 'devtools' package as it allows you to rapidly iterate while developing a package.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/pkgload, https://pkgload.r-lib.org", + "BugReports": "https://github.com/r-lib/pkgload/issues", + "Depends": [ + "R (>= 3.4.0)" + ], + "Imports": [ + "cli (>= 3.3.0)", + "desc", + "fs", + "glue", + "lifecycle", + "methods", + "pkgbuild", + "processx", + "rlang (>= 1.1.1)", + "rprojroot", + "utils" + ], + "Suggests": [ + "bitops", + "jsonlite", + "mathjaxr", + "pak", + "Rcpp", + "remotes", + "rstudioapi", + "testthat (>= 3.2.1.1)", + "usethis", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate, ggplot2", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "TRUE", + "Config/testthat/start-first": "dll", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut], Winston Chang [aut], Jim Hester [aut], Lionel Henry [aut, cre], Posit Software, PBC [cph, fnd], R Core team [ctb] (Some namespace and vignette code extracted from base R)", + "Maintainer": "Lionel Henry ", + "Repository": "CRAN" + }, + "praise": { + "Package": "praise", + "Version": "1.0.0", + "Source": "Repository", + "Title": "Praise Users", + "Author": "Gabor Csardi, Sindre Sorhus", + "Maintainer": "Gabor Csardi ", + "Description": "Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.", + "License": "MIT + file LICENSE", + "LazyData": "true", + "URL": "https://github.com/gaborcsardi/praise", + "BugReports": "https://github.com/gaborcsardi/praise/issues", + "Suggests": [ + "testthat" + ], + "Collate": "'adjective.R' 'adverb.R' 'exclamation.R' 'verb.R' 'rpackage.R' 'package.R'", + "NeedsCompilation": "no", + "Repository": "CRAN" + }, + "prettyunits": { + "Package": "prettyunits", + "Version": "1.2.0", + "Source": "Repository", + "Title": "Pretty, Human Readable Formatting of Quantities", + "Authors@R": "c( person(\"Gabor\", \"Csardi\", email=\"csardi.gabor@gmail.com\", role=c(\"aut\", \"cre\")), person(\"Bill\", \"Denney\", email=\"wdenney@humanpredictions.com\", role=c(\"ctb\"), comment=c(ORCID=\"0000-0002-5759-428X\")), person(\"Christophe\", \"Regouby\", email=\"christophe.regouby@free.fr\", role=c(\"ctb\")) )", + "Description": "Pretty, human readable formatting of quantities. Time intervals: '1337000' -> '15d 11h 23m 20s'. Vague time intervals: '2674000' -> 'about a month ago'. Bytes: '1337' -> '1.34 kB'. Rounding: '99' with 3 significant digits -> '99.0' p-values: '0.00001' -> '<0.0001'. Colors: '#FF0000' -> 'red'. Quantities: '1239437' -> '1.24 M'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/prettyunits", + "BugReports": "https://github.com/r-lib/prettyunits/issues", + "Depends": [ + "R(>= 2.10)" + ], + "Suggests": [ + "codetools", + "covr", + "testthat" + ], + "RoxygenNote": "7.2.3", + "Encoding": "UTF-8", + "NeedsCompilation": "no", + "Author": "Gabor Csardi [aut, cre], Bill Denney [ctb] (), Christophe Regouby [ctb]", + "Maintainer": "Gabor Csardi ", + "Repository": "CRAN" + }, + "processx": { + "Package": "processx", + "Version": "3.9.0", + "Source": "Repository", + "Title": "Execute and Control System Processes", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0001-7098-9676\")), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")), person(\"Ascent Digital Services\", role = c(\"cph\", \"fnd\")) )", + "Description": "Tools to run system processes in the background. It can check if a background process is running; wait on a background process to finish; get the exit status of finished processes; kill background processes. It can read the standard output and error of the processes, using non-blocking connections. 'processx' can poll a process for standard output or error, with a timeout. It can also poll several processes at once.", + "License": "MIT + file LICENSE", + "URL": "https://processx.r-lib.org, https://github.com/r-lib/processx", + "BugReports": "https://github.com/r-lib/processx/issues", + "Depends": [ + "R (>= 3.4.0)" + ], + "Imports": [ + "ps (>= 1.9.3)", + "R6", + "utils" + ], + "Suggests": [ + "callr (>= 3.7.3)", + "cli (>= 3.3.0)", + "codetools", + "covr", + "curl", + "debugme", + "parallel", + "rlang (>= 1.0.2)", + "testthat (>= 3.0.0)", + "webfakes", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-25", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Gábor Csárdi [aut, cre, cph] (ORCID: ), Winston Chang [aut], Posit Software, PBC [cph, fnd] (ROR: ), Ascent Digital Services [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "progress": { + "Package": "progress", + "Version": "1.2.3", + "Source": "Repository", + "Title": "Terminal Progress Bars", + "Authors@R": "c( person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Rich\", \"FitzJohn\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "Configurable Progress bars, they may include percentage, elapsed time, and/or the estimated completion time. They work in terminals, in 'Emacs' 'ESS', 'RStudio', 'Windows' 'Rgui' and the 'macOS' 'R.app'. The package also provides a 'C++' 'API', that works with or without 'Rcpp'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/progress#readme, http://r-lib.github.io/progress/", + "BugReports": "https://github.com/r-lib/progress/issues", + "Depends": [ + "R (>= 3.6)" + ], + "Imports": [ + "crayon", + "hms", + "prettyunits", + "R6" + ], + "Suggests": [ + "Rcpp", + "testthat (>= 3.0.0)", + "withr" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.2.3", + "NeedsCompilation": "no", + "Author": "Gábor Csárdi [aut, cre], Rich FitzJohn [aut], Posit Software, PBC [cph, fnd]", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "progressr": { + "Package": "progressr", + "Version": "1.0.0", + "Source": "Repository", + "Title": "An Inclusive, Unifying API for Progress Updates", + "Description": "A minimal, unifying API for scripts and packages to report progress updates from anywhere including when using parallel processing. The package is designed such that the developer can to focus on what progress should be reported on without having to worry about how to present it. The end user has full control of how, where, and when to render these progress updates, e.g. in the terminal using utils::txtProgressBar(), cli::cli_progress_bar(), in a graphical user interface using utils::winProgressBar(), tcltk::tkProgressBar() or shiny::withProgress(), via the speakers using beepr::beep(), or on a file system via the size of a file. Anyone can add additional, customized, progression handlers. The 'progressr' package uses R's condition framework for signaling progress updated. Because of this, progress can be reported from almost anywhere in R, e.g. from classical for and while loops, from map-reduce API:s like the lapply() family of functions, 'purrr', 'plyr', and 'foreach'. It will also work with parallel processing via the 'future' framework, e.g. 'lapply(...) |> futurize()' and 'purrr::map(...) |> futurize()', which uses future.apply::future_lapply() and furrr::future_map() internally. The package is compatible with Shiny applications.", + "Authors@R": "c(person(\"Henrik\", \"Bengtsson\", role = c(\"aut\", \"cre\", \"cph\"), email = \"henrikb@braju.com\", comment = c(ORCID = \"0000-0002-7579-5165\")))", + "License": "Apache License (>= 2)", + "Depends": [ + "R (>= 3.5.0)" + ], + "Imports": [ + "digest", + "utils" + ], + "Suggests": [ + "graphics", + "tcltk", + "beepr", + "cli", + "crayon", + "pbmcapply", + "progress", + "purrr", + "foreach", + "plyr", + "doFuture", + "future", + "future.apply", + "furrr", + "ntfy", + "RPushbullet", + "rstudioapi", + "shiny", + "commonmark", + "base64enc", + "tools" + ], + "VignetteBuilder": "progressr", + "Language": "en-US", + "Encoding": "UTF-8", + "URL": "https://progressr.futureverse.org, https://github.com/futureverse/progressr", + "BugReports": "https://github.com/futureverse/progressr/issues", + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "no", + "Author": "Henrik Bengtsson [aut, cre, cph] (ORCID: )", + "Maintainer": "Henrik Bengtsson ", + "Repository": "CRAN" + }, + "proxy": { + "Package": "proxy", + "Version": "0.4-29", + "Source": "Repository", + "Type": "Package", + "Title": "Distance and Similarity Measures", + "Authors@R": "c(person(given = \"David\", family = \"Meyer\", role = c(\"aut\", \"cre\"), email = \"David.Meyer@R-project.org\", comment = c(ORCID = \"0000-0002-5196-3048\")),\t person(given = \"Christian\", family = \"Buchta\", role = \"aut\"))", + "Description": "Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.", + "Depends": [ + "R (>= 3.4.0)" + ], + "Imports": [ + "stats", + "utils" + ], + "Suggests": [ + "cba" + ], + "Collate": "registry.R database.R dist.R similarities.R dissimilarities.R util.R seal.R", + "License": "GPL-2 | GPL-3", + "NeedsCompilation": "yes", + "Author": "David Meyer [aut, cre] (ORCID: ), Christian Buchta [aut]", + "Maintainer": "David Meyer ", + "Repository": "CRAN" + }, + "ps": { + "Package": "ps", + "Version": "1.9.3", + "Source": "Repository", + "Title": "List, Query, Manipulate System Processes", + "Authors@R": "c( person(\"Jay\", \"Loden\", role = \"aut\"), person(\"Dave\", \"Daeschler\", role = \"aut\"), person(\"Giampaolo\", \"Rodola'\", role = \"aut\"), person(\"Gábor\", \"Csárdi\", , \"csardi.gabor@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")) )", + "Description": "List, query and manipulate all system processes, on 'Windows', 'Linux' and 'macOS'.", + "License": "MIT + file LICENSE", + "URL": "https://github.com/r-lib/ps, https://ps.r-lib.org/", + "BugReports": "https://github.com/r-lib/ps/issues", + "Depends": [ + "R (>= 3.4)" + ], + "Imports": [ + "utils" + ], + "Suggests": [ + "callr", + "covr", + "curl", + "pillar", + "pingr", + "processx (>= 3.1.0)", + "R6", + "rlang", + "testthat (>= 3.0.0)", + "webfakes", + "withr" + ], + "Biarch": "true", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Config/usethis/last-upkeep": "2025-04-28", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Jay Loden [aut], Dave Daeschler [aut], Giampaolo Rodola' [aut], Gábor Csárdi [aut, cre], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Gábor Csárdi ", + "Repository": "CRAN" + }, + "psych": { + "Package": "psych", + "Version": "2.6.5", + "Source": "Repository", + "Date": "2026-05-11", + "Title": "Procedures for Psychological, Psychometric, and Personality Research", + "Authors@R": "person(\"William\", \"Revelle\", role =c(\"aut\",\"cre\"), email=\"revelle@northwestern.edu\", comment=c(ORCID = \"0000-0003-4880-9610\") )", + "Description": "A general purpose toolbox developed originally for personality, psychometric theory and experimental psychology. Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multiple levels include within and between group statistics, including correlations and factor analysis. Validation and cross validation of scales developed using basic machine learning algorithms are provided, as are functions for simulating and testing particular item and test structures. Several functions serve as a useful front end for structural equation modeling. Graphical displays of path diagrams, including mediation models, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometric theory as well as publications in personality research. For more information, see the web page.", + "License": "GPL (>= 2)", + "Imports": [ + "mnormt", + "parallel", + "stats", + "graphics", + "grDevices", + "methods", + "lattice", + "nlme", + "GPArotation" + ], + "Suggests": [ + "psychTools", + "lavaan", + "lme4", + "Rcsdp", + "graph", + "knitr", + "Rgraphviz" + ], + "LazyData": "yes", + "ByteCompile": "true", + "VignetteBuilder": "knitr", + "URL": "https://personality-project.org/r/psych/ https://personality-project.org/r/psych-manual.pdf", + "NeedsCompilation": "no", + "Author": "William Revelle [aut, cre] (ORCID: )", + "Maintainer": "William Revelle ", + "Repository": "CRAN" + }, + "purrr": { + "Package": "purrr", + "Version": "1.2.2", + "Source": "Repository", + "Title": "Functional Programming Tools", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = \"aut\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"https://ror.org/03wc8by49\")) )", + "Description": "A complete and consistent functional programming toolkit for R.", + "License": "MIT + file LICENSE", + "URL": "https://purrr.tidyverse.org/, https://github.com/tidyverse/purrr", + "BugReports": "https://github.com/tidyverse/purrr/issues", + "Depends": [ + "R (>= 4.1)" + ], + "Imports": [ + "cli (>= 3.6.1)", + "lifecycle (>= 1.0.3)", + "magrittr (>= 1.5.0)", + "rlang (>= 1.1.1)", + "vctrs (>= 0.6.3)" + ], + "Suggests": [ + "carrier (>= 0.3.0)", + "covr", + "dplyr (>= 0.7.8)", + "httr", + "knitr", + "lubridate", + "mirai (>= 2.5.1)", + "rmarkdown", + "testthat (>= 3.0.0)", + "tibble", + "tidyselect" + ], + "LinkingTo": [ + "cli" + ], + "VignetteBuilder": "knitr", + "Biarch": "true", + "Config/build/compilation-database": "true", + "Config/Needs/website": "tidyverse/tidytemplate, tidyr", + "Config/testthat/edition": "3", + "Config/testthat/parallel": "TRUE", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [aut, cre] (ORCID: ), Lionel Henry [aut], Posit Software, PBC [cph, fnd] (ROR: )", + "Maintainer": "Hadley Wickham ", + "Repository": "CRAN" + }, + "qs2": { + "Package": "qs2", + "Version": "0.3.1", + "Source": "Repository", + "Type": "Package", + "Title": "Efficient Serialization of R Objects", + "Date": "2026-08-19", + "Authors@R": "c( person(\"Travers\", \"Ching\", email = \"traversc@gmail.com\", role = c(\"aut\", \"cre\", \"cph\")), person(\"Yann\", \"Collet\", role = c(\"ctb\", \"cph\"), comment = \"Yann Collet is the author of the bundled zstd\"), person(\"Facebook, Inc.\", role = \"cph\", comment = \"Facebook is the copyright holder of the bundled zstd code\"), person(\"Reichardt\", \"Tino\", role = c(\"ctb\", \"cph\"), comment = \"Contributor/copyright holder of zstd bundled code\"), person(\"Skibinski\", \"Przemyslaw\", role = c(\"ctb\", \"cph\"), comment = \"Contributor/copyright holder of zstd bundled code\"), person(\"Mori\", \"Yuta\", role = c(\"ctb\", \"cph\"), comment = \"Contributor/copyright holder of zstd bundled code\"), person(\"Francesc\", \"Alted\", role = c(\"ctb\", \"cph\"), comment = \"Shuffling routines derived from Blosc library\"))", + "Maintainer": "Travers Ching ", + "Description": "Streamlines and accelerates the process of saving and loading R objects, improving speed and compression compared to other methods. The package provides two compression formats: the 'qs2' format, which uses R serialization via the C API while optimizing compression and disk I/O, and the 'qdata' format, featuring custom serialization for slightly faster performance and better compression. Additionally, the 'qs2' format can be directly converted to the standard 'RDS' format, ensuring long-term compatibility with future versions of R.", + "License": "GPL-3", + "LazyData": "true", + "Biarch": "true", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "Rcpp", + "RcppParallel (>= 6.1.1)", + "stringfish (>= 0.18.0)" + ], + "LinkingTo": [ + "Rcpp", + "RcppParallel (>= 6.1.1)" + ], + "Suggests": [ + "knitr", + "rmarkdown", + "dplyr", + "data.table", + "stringi" + ], + "SystemRequirements": "GNU make, C++17", + "Encoding": "UTF-8", + "VignetteBuilder": "knitr", + "Copyright": "This package includes code from the 'zstd' library owned by Facebook, Inc. and created by Yann Collet; and code derived from the 'Blosc' library created and owned by Francesc Alted.", + "URL": "https://github.com/qsbase/qs2", + "BugReports": "https://github.com/qsbase/qs2/issues", + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "yes", + "Author": "Travers Ching [aut, cre, cph], Yann Collet [ctb, cph] (Yann Collet is the author of the bundled zstd), Facebook, Inc. [cph] (Facebook is the copyright holder of the bundled zstd code), Reichardt Tino [ctb, cph] (Contributor/copyright holder of zstd bundled code), Skibinski Przemyslaw [ctb, cph] (Contributor/copyright holder of zstd bundled code), Mori Yuta [ctb, cph] (Contributor/copyright holder of zstd bundled code), Francesc Alted [ctb, cph] (Shuffling routines derived from Blosc library)", + "Repository": "CRAN" + }, + "quadprog": { + "Package": "quadprog", + "Version": "1.5-8", + "Source": "Repository", + "Type": "Package", + "Title": "Functions to Solve Quadratic Programming Problems", + "Date": "2019-11-20", + "Author": "S original by Berwin A. Turlach R port by Andreas Weingessel Fortran contributions from Cleve Moler (dposl/LINPACK and (a modified version of) dpodi/LINPACK)", + "Maintainer": "Berwin A. Turlach ", + "Description": "This package contains routines and documentation for solving quadratic programming problems.", + "Depends": [ + "R (>= 3.1.0)" + ], + "License": "GPL (>= 2)", + "NeedsCompilation": "yes", + "Repository": "CRAN" + }, + "readr": { + "Package": "readr", + "Version": "2.2.0", + "Source": "Repository", + "Title": "Read Rectangular Text Data", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Romain\", \"Francois\", role = \"ctb\"), person(\"Jennifer\", \"Bryan\", , \"jenny@posit.co\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0002-6983-2759\")), person(\"Shelby\", \"Bearrows\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\"), comment = c(ROR = \"03wc8by49\")), person(\"https://github.com/mandreyel/\", role = \"cph\", comment = \"mio library\"), person(\"Jukka\", \"Jylänki\", role = c(\"ctb\", \"cph\"), comment = \"grisu3 implementation\"), person(\"Mikkel\", \"Jørgensen\", role = c(\"ctb\", \"cph\"), comment = \"grisu3 implementation\") )", + "Description": "The goal of 'readr' is to provide a fast and friendly way to read rectangular data (like 'csv', 'tsv', and 'fwf'). 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Using 'renv', you can create and manage project-local R libraries, save the state of these libraries to a 'lockfile', and later restore your library as required. 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Designed particularly for use in testing packages where being able to quickly isolate key differences makes understanding test failures much easier.", + "License": "MIT + file LICENSE", + "URL": "https://waldo.r-lib.org, https://github.com/r-lib/waldo", + "BugReports": "https://github.com/r-lib/waldo/issues", + "Depends": [ + "R (>= 4.0)" + ], + "Imports": [ + "cli", + "diffobj (>= 0.3.4)", + "glue", + "methods", + "rlang (>= 1.1.0)" + ], + "Suggests": [ + "bit64", + "R6", + "S7", + "testthat (>= 3.0.0)", + "withr", + "xml2" + ], + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "NeedsCompilation": "no", + "Author": "Hadley Wickham [aut, cre], Posit Software, PBC [cph, fnd]", + "Maintainer": "Hadley Wickham ", + "Repository": "CRAN" + }, + "withr": { + "Package": "withr", + "Version": "3.0.3", + "Source": "Repository", + "Title": "Run Code 'With' Temporarily Modified Global State", + "Authors@R": "c( person(\"Jim\", \"Hester\", role = \"aut\"), person(\"Lionel\", \"Henry\", , \"lionel@posit.co\", role = c(\"aut\", \"cre\")), person(\"Kirill\", \"Müller\", , \"krlmlr+r@mailbox.org\", role = \"aut\"), person(\"Kevin\", \"Ushey\", , \"kevinushey@gmail.com\", role = \"aut\"), person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"aut\"), person(\"Winston\", \"Chang\", role = \"aut\"), person(\"Jennifer\", \"Bryan\", role = \"ctb\"), person(\"Richard\", \"Cotton\", role = \"ctb\"), person(\"Posit Software, PBC\", role = c(\"cph\", \"fnd\")) )", + "Description": "A set of functions to run code 'with' safely and temporarily modified global state. Many of these functions were originally a part of the 'devtools' package, this provides a simple package with limited dependencies to provide access to these functions.", + "License": "MIT + file LICENSE", + "URL": "https://withr.r-lib.org, https://github.com/r-lib/withr#readme", + "BugReports": "https://github.com/r-lib/withr/issues", + "Depends": [ + "R (>= 3.6.0)" + ], + "Imports": [ + "graphics", + "grDevices" + ], + "Suggests": [ + "callr", + "DBI", + "knitr", + "methods", + "rlang", + "rmarkdown (>= 2.12)", + "RSQLite", + "testthat (>= 3.0.0)" + ], + "VignetteBuilder": "knitr", + "Config/Needs/website": "tidyverse/tidytemplate", + "Config/testthat/edition": "3", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.2", + "Collate": "'aaa.R' 'collate.R' 'connection.R' 'db.R' 'defer-exit.R' 'standalone-defer.R' 'defer.R' 'devices.R' 'local_.R' 'with_.R' 'dir.R' 'env.R' 'file.R' 'language.R' 'libpaths.R' 'locale.R' 'makevars.R' 'namespace.R' 'options.R' 'par.R' 'path.R' 'rng.R' 'seed.R' 'wrap.R' 'sink.R' 'tempfile.R' 'timezone.R' 'torture.R' 'utils.R' 'with.R'", + "NeedsCompilation": "no", + "Author": "Jim Hester [aut], Lionel Henry [aut, cre], Kirill Müller [aut], Kevin Ushey [aut], Hadley Wickham [aut], Winston Chang [aut], Jennifer Bryan [ctb], Richard Cotton [ctb], Posit Software, PBC [cph, fnd]", + "Maintainer": "Lionel Henry ", + "Repository": "CRAN" + }, + "xfun": { + "Package": "xfun", + "Version": "0.60", + "Source": "Repository", + "Type": "Package", + "Title": "Supporting Functions for Packages Maintained by 'Yihui Xie'", + "Authors@R": "c( person(\"Yihui\", \"Xie\", role = c(\"aut\", \"cre\", \"cph\"), email = \"xie@yihui.name\", comment = c(ORCID = \"0000-0003-0645-5666\", URL = \"https://yihui.org\")), person(\"Wush\", \"Wu\", role = \"ctb\"), person(\"Daijiang\", \"Li\", role = \"ctb\"), person(\"Xianying\", \"Tan\", role = \"ctb\"), person(\"Salim\", \"Brüggemann\", role = \"ctb\", email = \"salim-b@pm.me\", comment = c(ORCID = \"0000-0002-5329-5987\")), person(\"Christophe\", \"Dervieux\", role = \"ctb\"), person() )", + "Description": "Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.", + "Depends": [ + "R (>= 3.2.0)" + ], + "Imports": [ + "grDevices", + "stats", + "tools" + ], + "Suggests": [ + "testit", + "parallel", + "codetools", + "methods", + "rstudioapi", + "tinytex (>= 0.30)", + "mime", + "litedown (>= 0.6)", + "commonmark", + "knitr (>= 1.50)", + "remotes", + "pak", + "curl", + "xml2", + "jsonlite", + "magick", + "yaml", + "data.table", + "qs2" + ], + "License": "MIT + file LICENSE", + "URL": "https://github.com/yihui/xfun", + "BugReports": "https://github.com/yihui/xfun/issues", + "Encoding": "UTF-8", + "VignetteBuilder": "litedown", + "Config/roxygen2/version": "8.0.0", + "NeedsCompilation": "yes", + "Author": "Yihui Xie [aut, cre, cph] (ORCID: , URL: https://yihui.org), Wush Wu [ctb], Daijiang Li [ctb], Xianying Tan [ctb], Salim Brüggemann [ctb] (ORCID: ), Christophe Dervieux [ctb]", + "Maintainer": "Yihui Xie ", + "Repository": "CRAN" + }, + "yaml": { + "Package": "yaml", + "Version": "2.3.12", + "Source": "Repository", + "Type": "Package", + "Title": "Methods to Convert R Data to YAML and Back", + "Authors@R": "c( person(\"Hadley\", \"Wickham\", , \"hadley@posit.co\", role = \"cre\", comment = c(ORCID = \"0000-0003-4757-117X\")), person(\"Shawn\", \"Garbett\", , \"shawn.garbett@vumc.org\", role = \"ctb\", comment = c(ORCID = \"0000-0003-4079-5621\")), person(\"Jeremy\", \"Stephens\", role = c(\"aut\", \"ctb\")), person(\"Kirill\", \"Simonov\", role = \"aut\"), person(\"Yihui\", \"Xie\", role = \"ctb\", comment = c(ORCID = \"0000-0003-0645-5666\")), person(\"Zhuoer\", \"Dong\", role = \"ctb\"), person(\"Jeffrey\", \"Horner\", role = \"ctb\"), person(\"reikoch\", role = \"ctb\"), person(\"Will\", \"Beasley\", role = \"ctb\", comment = c(ORCID = \"0000-0002-5613-5006\")), person(\"Brendan\", \"O'Connor\", role = \"ctb\"), person(\"Michael\", \"Quinn\", role = \"ctb\"), person(\"Charlie\", \"Gao\", role = \"ctb\"), person(c(\"Gregory\", \"R.\"), \"Warnes\", role = \"ctb\"), person(c(\"Zhian\", \"N.\"), \"Kamvar\", role = \"ctb\") )", + "Description": "Implements the 'libyaml' 'YAML' 1.1 parser and emitter () for R.", + "License": "BSD_3_clause + file LICENSE", + "URL": "https://yaml.r-lib.org, https://github.com/r-lib/yaml/", + "BugReports": "https://github.com/r-lib/yaml/issues", + "Suggests": [ + "knitr", + "rmarkdown", + "testthat (>= 3.0.0)" + ], + "Config/testthat/edition": "3", + "Config/Needs/website": "tidyverse/tidytemplate", + "Encoding": "UTF-8", + "RoxygenNote": "7.3.3", + "VignetteBuilder": "knitr", + "NeedsCompilation": "yes", + "Author": "Hadley Wickham [cre] (ORCID: ), Shawn Garbett [ctb] (ORCID: ), Jeremy Stephens [aut, ctb], Kirill Simonov [aut], Yihui Xie [ctb] (ORCID: ), Zhuoer Dong [ctb], Jeffrey Horner [ctb], reikoch [ctb], Will Beasley [ctb] (ORCID: ), Brendan O'Connor [ctb], Michael Quinn [ctb], Charlie Gao [ctb], Gregory R. Warnes [ctb], Zhian N. Kamvar [ctb]", + "Maintainer": "Hadley Wickham ", + "Repository": "CRAN" + } + } +} diff --git a/research/environment/smoke-test-result.json b/research/environment/smoke-test-result.json new file mode 100644 index 0000000..7da3dec --- /dev/null +++ b/research/environment/smoke-test-result.json @@ -0,0 +1,140 @@ +{ + "python": "3.12.14 (main, Aug 14 2026, 15:40:22) [MSC v.1944 64 bit (AMD64)]", + "imports": { + "numpy": { + "status": "passed", + "version": "2.5.3" + }, + "scipy": { + "status": "passed", + "version": "1.18.1" + }, + "pandas": { + "status": "passed", + "version": "3.0.6" + }, + "polars": { + "status": "passed", + "version": "1.44.2" + }, + "pyarrow": { + "status": "passed", + "version": "25.0.1" + }, + "duckdb": { + "status": "passed", + "version": "1.5.5" + }, + "statsmodels": { + "status": "passed", + "version": "0.15.0" + }, + "pymc": { + "status": "passed", + "version": "6.3.2" + }, + "pytensor": { + "status": "passed", + "version": "3.3.2" + }, + "arviz": { + "status": "passed", + "version": "1.3.0" + }, + "nutpie": { + "status": "passed", + "version": "0.16.11" + }, + "jax": { + "status": "passed", + "version": "0.11.2" + }, + "numpyro": { + "status": "passed", + "version": "0.22.0" + }, + "blackjax": { + "status": "passed", + "version": "1.6.2" + }, + "bambi": { + "status": "passed", + "version": "0.21.0" + }, + "semopy": { + "status": "passed", + "version": "2.3.11" + }, + "factor_analyzer": { + "status": "passed", + "version": "unknown" + }, + "pingouin": { + "status": "passed", + "version": "0.6.1" + }, + "girth": { + "status": "passed", + "version": "unknown" + }, + "torch": { + "status": "passed", + "version": "2.14.0+cu130" + }, + "torchvision": { + "status": "passed", + "version": "0.29.0+cu130" + }, + "transformers": { + "status": "passed", + "version": "5.17.0" + }, + "sentence_transformers": { + "status": "passed", + "version": "6.1.0" + }, + "xgboost": { + "status": "passed", + "version": "3.4.1" + }, + "tabpfn": { + "status": "passed", + "version": "9.0.0" + } + }, + "tests": { + "pymc_sampling": { + "status": "passed", + "detail": { + "posterior_mean": 0.15326990867173734, + "draws": 50, + "chains": 1 + } + }, + "semopy_cfa": { + "status": "passed", + "detail": { + "success": true, + "objective": 8.346892954591567e-08 + } + }, + "factor_analyzer": { + "status": "passed", + "detail": { + "loading_shape": [ + 4, + 1 + ] + } + }, + "torch_cuda": { + "status": "passed", + "detail": { + "torch": "2.14.0+cu130", + "cuda_runtime": "13.0", + "device": "NVIDIA GeForce RTX 5060 Ti", + "finite": true + } + } + } +} \ No newline at end of file diff --git a/research/environment/smoke_test.py b/research/environment/smoke_test.py new file mode 100644 index 0000000..0bc0c08 --- /dev/null +++ b/research/environment/smoke_test.py @@ -0,0 +1,145 @@ +import importlib +import json +import os +from pathlib import Path +import sys +import traceback + + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +CACHE_ROOT = PROJECT_ROOT / ".cache" +cache_settings = { + "LOCALAPPDATA": CACHE_ROOT / "localappdata", + "MPLCONFIGDIR": CACHE_ROOT / "matplotlib", + "NUMBA_CACHE_DIR": CACHE_ROOT / "numba", + "SKB_DATA_DIRECTORY": CACHE_ROOT / "skrub", + "HF_HOME": CACHE_ROOT / "huggingface", + "TORCH_HOME": CACHE_ROOT / "torch", + "XDG_CACHE_HOME": CACHE_ROOT, +} +for key, path in cache_settings.items(): + path.mkdir(parents=True, exist_ok=True) + os.environ[key] = str(path) +os.environ["PYTENSOR_FLAGS"] = f"base_compiledir={CACHE_ROOT / 'pytensor'}" + +import numpy as np + + +results = {"python": sys.version, "imports": {}, "tests": {}} + + +def record(name, fn): + try: + value = fn() + results["tests"][name] = {"status": "passed", "detail": value} + except Exception as exc: + results["tests"][name] = { + "status": "failed", + "detail": f"{type(exc).__name__}: {exc}", + "traceback": traceback.format_exc(), + } + + +for module_name in [ + "numpy", "scipy", "pandas", "polars", "pyarrow", "duckdb", + "statsmodels", "pymc", "pytensor", "arviz", "nutpie", + "jax", "numpyro", "blackjax", "bambi", "semopy", + "factor_analyzer", "pingouin", "girth", "torch", "torchvision", + "transformers", "sentence_transformers", "xgboost", + "tabpfn", +]: + try: + module = importlib.import_module(module_name) + results["imports"][module_name] = { + "status": "passed", + "version": getattr(module, "__version__", "unknown"), + } + except Exception as exc: + results["imports"][module_name] = { + "status": "failed", + "detail": f"{type(exc).__name__}: {exc}", + } + + +def pymc_test(): + import pymc as pm + + rng = np.random.default_rng(20260920) + observed = rng.normal(0.35, 1.0, size=40) + with pm.Model(): + mu = pm.Normal("mu", 0.0, 1.0) + pm.Normal("y", mu, 1.0, observed=observed) + inference = pm.sample( + draws=50, + tune=50, + chains=1, + cores=1, + random_seed=20260920, + progressbar=False, + compute_convergence_checks=False, + ) + posterior_mean = float(inference.posterior["mu"].mean()) + return {"posterior_mean": posterior_mean, "draws": 50, "chains": 1} + + +def sem_test(): + import pandas as pd + import semopy + + rng = np.random.default_rng(20260920) + latent = rng.normal(size=160) + frame = pd.DataFrame( + { + "x1": 0.8 * latent + rng.normal(scale=0.4, size=160), + "x2": 0.7 * latent + rng.normal(scale=0.5, size=160), + "x3": 0.9 * latent + rng.normal(scale=0.3, size=160), + } + ) + model = semopy.Model("factor =~ x1 + x2 + x3") + fit = model.fit(frame) + return {"success": bool(fit.success), "objective": float(fit.fun)} + + +def factor_test(): + from factor_analyzer import FactorAnalyzer + + rng = np.random.default_rng(20260920) + latent = rng.normal(size=(180, 1)) + data = latent @ np.array([[0.8, 0.7, 0.9, 0.6]]) + rng.normal( + scale=0.45, size=(180, 4) + ) + fitted = FactorAnalyzer(n_factors=1, rotation=None).fit(data) + return {"loading_shape": list(fitted.loadings_.shape)} + + +def torch_test(): + import torch + + if not torch.cuda.is_available(): + raise RuntimeError("torch.cuda.is_available() is false") + left = torch.randn((256, 256), device="cuda") + right = torch.randn((256, 256), device="cuda") + result = left @ right + torch.cuda.synchronize() + return { + "torch": torch.__version__, + "cuda_runtime": torch.version.cuda, + "device": torch.cuda.get_device_name(0), + "finite": bool(torch.isfinite(result).all().item()), + } + + +record("pymc_sampling", pymc_test) +record("semopy_cfa", sem_test) +record("factor_analyzer", factor_test) +record("torch_cuda", torch_test) + +serialized = json.dumps(results, ensure_ascii=False, indent=2) +(Path(__file__).parent / "smoke-test-result.json").write_text( + serialized, encoding="utf-8" +) +print(serialized) + +failed_imports = [k for k, v in results["imports"].items() if v["status"] != "passed"] +failed_tests = [k for k, v in results["tests"].items() if v["status"] != "passed"] +raise SystemExit(1 if failed_imports or failed_tests else 0) diff --git a/research/experiments/build_literature_matrix.mjs b/research/experiments/build_literature_matrix.mjs new file mode 100644 index 0000000..8b186ef --- /dev/null +++ b/research/experiments/build_literature_matrix.mjs @@ -0,0 +1,77 @@ +import fs from "node:fs/promises"; +import { SpreadsheetFile, Workbook } from "@oai/artifact-tool"; + +const headers = [ + "source_id", "citation", "year", "publication_status", "evidence_type", + "domain_and_data", "sample_or_scope", "main_result", "project_use", + "limitations", "supports", "does_not_support", "doi_or_id", "source_url", + "verification_status", "checked_date" +]; + +const rows = [ + ["L01", "Chen et al. Pre-statistical harmonization of behavioral instruments across eight surveys and trials", 2021, "peer_reviewed", "empirical workflow/methods", "Behavioral instruments across eight dementia surveys and trials", "Eight studies; manual instrument review plus automated raw-data checks", "Comparable-looking items often differed in wording, response options, scoring, or direction and required pre-statistical review", "Defines the source-review and crosswalk work required before statistical linking", "Different population and constructs; does not test semantic embeddings or cross-national DIF", "Official wording, response options, scoring, and populations must be reviewed before pooling", "Semantic similarity alone establishes psychometric equivalence", "10.1186/s12874-021-01431-6", "https://doi.org/10.1186/s12874-021-01431-6", "verified_primary", "2026-09-20"], + ["L02", "Kołczyńska. Combining multiple survey sources: A reproducible workflow and toolbox for survey data harmonization", 2022, "peer_reviewed", "methods/workflow", "Four cross-national survey projects; trust items", "ESS, EVS, EQLS and Eurobarometer example", "Crosswalk-centered, human-auditable documentation improves reproducibility of ex-post harmonization", "Supports machine-readable source crosswalks, recodes, provenance and status tracking", "Focuses recoding/documentation rather than latent linking or item semantics", "Harmonization decisions and transformations need reusable documentation", "A documented crosswalk proves measurement invariance", "10.1177/20597991221077923", "https://doi.org/10.1177/20597991221077923", "verified_primary", "2026-09-20"], + ["L03", "McElroy et al. Using natural language processing to facilitate the harmonisation of mental health questionnaires", 2024, "peer_reviewed", "empirical validation", "Five mental-health questionnaires in a UK adult sample", "2,058 participants; 741 item pairs", "Sentence-BERT semantic similarity correlated moderately with empirical item correlations and predicted held-out pair correlations with small error", "Closest evidence for semantic item matching and response-structure signal", "Adult UK sample, overlapping questionnaires and shared respondents; manual rules still needed; no cross-country DIF or survey-design inference", "Text embeddings can help propose harmonization candidates", "Embedding similarity verifies psychometric equivalence or transportability", "10.1186/s12888-024-05954-2", "https://doi.org/10.1186/s12888-024-05954-2", "verified_primary", "2026-09-20"], + ["L04", "Ravenda et al. Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires", 2025, "peer_reviewed", "empirical proof-of-concept", "Big Five, DASS-42, GAD-7 and PHQ-9 questionnaire data", "Large public questionnaire datasets; proof-of-concept response prediction", "Semantic structure predicted empirical correlation patterns and supported prediction of responses to unseen items", "Shows semantic representations can encode response-structure information in psychological questionnaires", "Cross-cultural and multilingual transport were not established; predictive proof-of-concept is not survey harmonization", "Item semantics may explain part of response covariance", "Universal psychometric equivalence, DIF recovery or calibrated cross-national latent scores", "10.1038/s41598-025-21289-8", "https://doi.org/10.1038/s41598-025-21289-8", "verified_primary", "2026-09-20"], + ["L05", "Yancey et al. BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models", 2024, "peer_reviewed_conference", "method plus operational evaluation", "Duolingo English Test items", "High-stakes language assessment item bank; exact proprietary sample details require full-paper extraction", "BERT embeddings and engineered features reduced pilot length while maintaining reported criterion validity and reliability", "Direct precedent for text features predicting IRT item parameters", "Educational test items differ from suicide-related survey items; does not address cross-country DIF, complex samples or latent phenotype harmonization", "Text-derived item features can inform item-parameter estimation", "This project's core method is unprecedented or immediately transferable to health surveys", "ACL Anthology 2024.bea-1.35", "https://aclanthology.org/2024.bea-1.35/", "verified_primary", "2026-09-20"], + ["L06", "Chen and Chen. From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings", 2026, "preprint", "method/benchmark", "Mathematics and medical-licensure item banks", "Two item banks; repeated cross-validation and simulation-based ceilings", "Difficulty was more predictable than other parameters; reliability/design ceilings and repeated splits changed interpretation", "Requires uncertainty-aware targets, repeated grouped validation and ceiling analysis for semantic parameter prediction", "Preprint; educational/assessment domains; not cross-national mental-health surveys", "Parameter-prediction benchmarks need target reliability and design ceilings", "Reported RMSE alone establishes useful semantic signal", "arXiv:2607.07141", "https://arxiv.org/abs/2607.07141", "verified_preprint", "2026-09-20"], + ["L07", "Peters et al. Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review", 2025, "preprint", "systematic review", "Automated item-difficulty prediction", "37 articles through May 2025", "Language models can predict item difficulty in some settings, but studies vary in datasets, splits, targets and metrics", "Maps existing text-to-difficulty literature and prevents novelty overclaiming", "Preprint; focuses large-scale assessment rather than health questionnaires, DIF or survey design", "Text-based difficulty prediction is an established research area", "Reported best-case metrics transfer to this project", "arXiv:2509.23486", "https://arxiv.org/abs/2509.23486", "verified_preprint", "2026-09-20"], + ["L08", "Muthén and Asparouhov. IRT studies of many groups: the alignment method", 2014, "peer_reviewed", "method plus Monte Carlo", "Binary knowledge items across many country groups", "Two surveys plus simulation", "Alignment estimates group factor means/variances without requiring exact invariance and reports parameter non-invariance", "Core comparator for many-country measurement invariance and DIF", "Requires a prespecified factor structure and adequate linkage; alignment is not proof that all groups share one construct", "Approximate invariance can be studied across many groups", "Alignment repairs absent empirical connections or identifies a scale from semantics alone", "10.3389/fpsyg.2014.00978", "https://doi.org/10.3389/fpsyg.2014.00978", "verified_primary", "2026-09-20"], + ["L09", "Mansolf et al. Extensions of Multiple-Group Item Response Theory Alignment", 2020, "peer_reviewed", "method, simulation and application", "International psychiatric genomics consortium with disparate item sets and formats", "Multiple sites/instruments plus real-data-based simulation", "Extended alignment accommodated differing item sets and response categories and recovered parameters in simulation", "Closest latent-harmonization comparator for psychiatric phenotypes with nonidentical instruments", "Needs specified construct/factor model and empirical connections; population and sampling designs differ from this project", "Disparate psychiatric item sets can sometimes be aligned with explicit assumptions", "Semantic priors alone create a common scale or eliminate anchor requirements", "10.1177/0013164419897307", "https://doi.org/10.1177/0013164419897307", "verified_primary", "2026-09-20"], + ["L10", "Heinz et al. Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries", 2022, "peer_reviewed", "cross-national psychometric application", "Eight-item adolescent HBSC symptom checklist", "229,906 adolescents across 46 countries", "Configural/metric invariance was more defensible than scalar invariance; alignment identified item non-invariance", "Demonstrates the scale of cross-country adolescent DIF and consequences for comparisons", "Uses one established common instrument, not different survey tools or unseen-item semantic prediction", "Cross-national adolescent comparisons require item-level invariance/DIF checks", "A common questionnaire automatically yields scalar comparability", "10.1186/s12874-022-01698-3", "https://doi.org/10.1186/s12874-022-01698-3", "verified_primary", "2026-09-20"], + ["L11", "Savitsky and Williams. Pseudo Bayesian Mixed Models under Informative Sampling", 2022, "peer_reviewed", "method, simulation and application", "Hierarchical models under informative multistage sampling", "Simulation plus business-establishment survey example", "Weighting only unit likelihood contributions can remain biased when random effects correlate with design; weighting random-effect distributions addresses this setting", "Constrains how hierarchical country/survey effects and design weights can be combined", "Not an IRT application; requires inclusion-probability information and design assumptions", "Complex-sample Bayesian multilevel models need design-aware treatment beyond naive weighted likelihood", "Multiplying every likelihood by a weight guarantees correct interval coverage", "10.2478/jos-2022-0039", "https://doi.org/10.2478/jos-2022-0039", "verified_primary", "2026-09-20"], + ["L12", "Wu and Stephenson. Bayesian estimation methods for survey data with potential applications to health disparities research", 2024, "peer_reviewed_review", "narrative methodological review", "Bayesian analysis of complex survey data", "Reviews MRP, weighted pseudo-likelihood and synthetic-population approaches", "No single Bayesian survey method is universally sufficient; assumptions and target estimands determine the route", "Provides the survey-design method map for later model specifications and sensitivity analyses", "Review rather than project-specific validation; does not resolve IRT identification", "Multiple defensible Bayesian survey strategies exist and must be chosen by estimand/design", "Bayesian modeling automatically corrects informative sampling", "10.1002/wics.1633", "https://doi.org/10.1002/wics.1633", "verified_primary", "2026-09-20"], + ["L13", "Wu et al. Statistical harmonization of versions of measures across studies using external data", 2025, "peer_reviewed", "calibration-sample method", "Self-rated health and memory measured with different response formats", "External calibration sample of 300 participants", "A bridge sample answering both versions enabled model-based statistical harmonization with moderate agreement", "Shows why bridge data may be necessary when archival surveys lack empirical links", "Different constructs and older clinical population; external sample design differs from multi-item IRT", "Targeted bridge data can identify transformations unavailable from disconnected archives", "Text similarity can replace empirical bridge data without uncertainty", "10.1016/j.annepidem.2025.01.002", "https://doi.org/10.1016/j.annepidem.2025.01.002", "verified_primary", "2026-09-20"] +]; + +const csvEscape = (value) => { + const text = String(value ?? ""); + return /[",\r\n]/.test(text) ? `"${text.replaceAll('"', '""')}"` : text; +}; + +const csv = [headers, ...rows].map((row) => row.map(csvEscape).join(",")).join("\r\n") + "\r\n"; +const outputDir = new URL("../protocol/", import.meta.url); +await fs.mkdir(outputDir, { recursive: true }); +await fs.writeFile(new URL("literature_matrix.csv", outputDir), csv, "utf8"); + +const workbook = Workbook.create(); +const sheet = workbook.worksheets.add("Literature Matrix"); +sheet.showGridLines = false; +sheet.getRange("A1:P1").merge(); +sheet.getRange("A1").values = [["Focused literature matrix"]]; +sheet.getRange("A2:P2").merge(); +sheet.getRange("A2").values = [["Questionnaire harmonization, semantic item models, cross-national DIF, and complex-survey Bayesian inference. Checked 2026-09-20."]]; +sheet.getRange("A4:P17").values = [headers, ...rows]; +sheet.tables.add("A4:P17", true, "LiteratureMatrix"); +sheet.freezePanes.freezeRows(4); +sheet.freezePanes.freezeColumns(2); +sheet.getRange("A1:P17").format.font = { name: "Arial", size: 10 }; +sheet.getRange("A1").format.font = { name: "Arial", size: 15, bold: true, color: "#1F2937" }; +sheet.getRange("A2").format.font = { name: "Arial", size: 10, italic: true, color: "#4B5563" }; +sheet.getRange("A4:P4").format = { fill: "#1F4E78", font: { name: "Arial", size: 10, bold: true, color: "#FFFFFF" }, wrapText: true, verticalAlignment: "center" }; +sheet.getRange("A5:P17").format.wrapText = true; +sheet.getRange("A5:P17").format.verticalAlignment = "top"; +sheet.getRange("A4:P17").format.borders = { insideHorizontal: { style: "thin", color: "#D9E2F3" }, bottom: { style: "thin", color: "#B4C6E7" } }; +sheet.getRange("A:A").format.columnWidth = 10; +sheet.getRange("B:B").format.columnWidth = 40; +sheet.getRange("C:C").format.columnWidth = 8; +sheet.getRange("D:E").format.columnWidth = 20; +sheet.getRange("F:F").format.columnWidth = 36; +sheet.getRange("G:G").format.columnWidth = 34; +sheet.getRange("H:L").format.columnWidth = 44; +sheet.getRange("M:M").format.columnWidth = 24; +sheet.getRange("N:N").format.columnWidth = 42; +sheet.getRange("O:O").format.columnWidth = 18; +sheet.getRange("P:P").format.columnWidth = 13; +sheet.getRange("C5:C17").format.numberFormat = "0"; +sheet.getRange("P5:P17").format.numberFormat = "yyyy-mm-dd"; + +const check = await workbook.inspect({ kind: "table", range: "Literature Matrix!A4:P17", include: "values,formulas", tableMaxRows: 16, tableMaxCols: 16, maxChars: 12000 }); +console.log(check.ndjson); +const errors = await workbook.inspect({ kind: "match", searchTerm: "#REF!|#DIV/0!|#VALUE!|#NAME\\?|#N/A|#NUM!|#NULL!|#SPILL!|#CALC!", options: { useRegex: true, maxResults: 100 }, summary: "final formula error scan" }); +console.log(errors.ndjson); +const preview = await workbook.render({ sheetName: "Literature Matrix", range: "A1:P17", scale: 0.8, format: "png" }); +await fs.writeFile(new URL("literature_matrix_preview.png", outputDir), new Uint8Array(await preview.arrayBuffer())); +const xlsx = await SpreadsheetFile.exportXlsx(workbook); +await xlsx.save(new URL("literature_matrix.xlsx", outputDir).pathname.replace(/^\/(.:)/, "$1")); + diff --git a/research/protocol/literature_matrix.csv b/research/protocol/literature_matrix.csv new file mode 100644 index 0000000..7eb23c9 --- /dev/null +++ b/research/protocol/literature_matrix.csv @@ -0,0 +1,14 @@ +source_id,citation,year,publication_status,evidence_type,domain_and_data,sample_or_scope,main_result,project_use,limitations,supports,does_not_support,doi_or_id,source_url,verification_status,checked_date +L01,Chen et al. Pre-statistical harmonization of behavioral instruments across eight surveys and trials,2021,peer_reviewed,empirical workflow/methods,Behavioral instruments across eight dementia surveys and trials,Eight studies; manual instrument review plus automated raw-data checks,"Comparable-looking items often differed in wording, response options, scoring, or direction and required pre-statistical review",Defines the source-review and crosswalk work required before statistical linking,Different population and constructs; does not test semantic embeddings or cross-national DIF,"Official wording, response options, scoring, and populations must be reviewed before pooling",Semantic similarity alone establishes psychometric equivalence,10.1186/s12874-021-01431-6,https://doi.org/10.1186/s12874-021-01431-6,verified_primary,2026-09-20 +L02,Kołczyńska. Combining multiple survey sources: A reproducible workflow and toolbox for survey data harmonization,2022,peer_reviewed,methods/workflow,Four cross-national survey projects; trust items,"ESS, EVS, EQLS and Eurobarometer example","Crosswalk-centered, human-auditable documentation improves reproducibility of ex-post harmonization","Supports machine-readable source crosswalks, recodes, provenance and status tracking",Focuses recoding/documentation rather than latent linking or item semantics,Harmonization decisions and transformations need reusable documentation,A documented crosswalk proves measurement invariance,10.1177/20597991221077923,https://doi.org/10.1177/20597991221077923,verified_primary,2026-09-20 +L03,McElroy et al. Using natural language processing to facilitate the harmonisation of mental health questionnaires,2024,peer_reviewed,empirical validation,Five mental-health questionnaires in a UK adult sample,"2,058 participants; 741 item pairs",Sentence-BERT semantic similarity correlated moderately with empirical item correlations and predicted held-out pair correlations with small error,Closest evidence for semantic item matching and response-structure signal,"Adult UK sample, overlapping questionnaires and shared respondents; manual rules still needed; no cross-country DIF or survey-design inference",Text embeddings can help propose harmonization candidates,Embedding similarity verifies psychometric equivalence or transportability,10.1186/s12888-024-05954-2,https://doi.org/10.1186/s12888-024-05954-2,verified_primary,2026-09-20 +L04,Ravenda et al. Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires,2025,peer_reviewed,empirical proof-of-concept,"Big Five, DASS-42, GAD-7 and PHQ-9 questionnaire data",Large public questionnaire datasets; proof-of-concept response prediction,Semantic structure predicted empirical correlation patterns and supported prediction of responses to unseen items,Shows semantic representations can encode response-structure information in psychological questionnaires,Cross-cultural and multilingual transport were not established; predictive proof-of-concept is not survey harmonization,Item semantics may explain part of response covariance,"Universal psychometric equivalence, DIF recovery or calibrated cross-national latent scores",10.1038/s41598-025-21289-8,https://doi.org/10.1038/s41598-025-21289-8,verified_primary,2026-09-20 +L05,Yancey et al. BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models,2024,peer_reviewed_conference,method plus operational evaluation,Duolingo English Test items,High-stakes language assessment item bank; exact proprietary sample details require full-paper extraction,BERT embeddings and engineered features reduced pilot length while maintaining reported criterion validity and reliability,Direct precedent for text features predicting IRT item parameters,"Educational test items differ from suicide-related survey items; does not address cross-country DIF, complex samples or latent phenotype harmonization",Text-derived item features can inform item-parameter estimation,This project's core method is unprecedented or immediately transferable to health surveys,ACL Anthology 2024.bea-1.35,https://aclanthology.org/2024.bea-1.35/,verified_primary,2026-09-20 +L06,Chen and Chen. From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings,2026,preprint,method/benchmark,Mathematics and medical-licensure item banks,Two item banks; repeated cross-validation and simulation-based ceilings,Difficulty was more predictable than other parameters; reliability/design ceilings and repeated splits changed interpretation,"Requires uncertainty-aware targets, repeated grouped validation and ceiling analysis for semantic parameter prediction",Preprint; educational/assessment domains; not cross-national mental-health surveys,Parameter-prediction benchmarks need target reliability and design ceilings,Reported RMSE alone establishes useful semantic signal,arXiv:2607.07141,https://arxiv.org/abs/2607.07141,verified_preprint,2026-09-20 +L07,Peters et al. Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review,2025,preprint,systematic review,Automated item-difficulty prediction,37 articles through May 2025,"Language models can predict item difficulty in some settings, but studies vary in datasets, splits, targets and metrics",Maps existing text-to-difficulty literature and prevents novelty overclaiming,"Preprint; focuses large-scale assessment rather than health questionnaires, DIF or survey design",Text-based difficulty prediction is an established research area,Reported best-case metrics transfer to this project,arXiv:2509.23486,https://arxiv.org/abs/2509.23486,verified_preprint,2026-09-20 +L08,Muthén and Asparouhov. IRT studies of many groups: the alignment method,2014,peer_reviewed,method plus Monte Carlo,Binary knowledge items across many country groups,Two surveys plus simulation,Alignment estimates group factor means/variances without requiring exact invariance and reports parameter non-invariance,Core comparator for many-country measurement invariance and DIF,Requires a prespecified factor structure and adequate linkage; alignment is not proof that all groups share one construct,Approximate invariance can be studied across many groups,Alignment repairs absent empirical connections or identifies a scale from semantics alone,10.3389/fpsyg.2014.00978,https://doi.org/10.3389/fpsyg.2014.00978,verified_primary,2026-09-20 +L09,Mansolf et al. Extensions of Multiple-Group Item Response Theory Alignment,2020,peer_reviewed,"method, simulation and application",International psychiatric genomics consortium with disparate item sets and formats,Multiple sites/instruments plus real-data-based simulation,Extended alignment accommodated differing item sets and response categories and recovered parameters in simulation,Closest latent-harmonization comparator for psychiatric phenotypes with nonidentical instruments,Needs specified construct/factor model and empirical connections; population and sampling designs differ from this project,Disparate psychiatric item sets can sometimes be aligned with explicit assumptions,Semantic priors alone create a common scale or eliminate anchor requirements,10.1177/0013164419897307,https://doi.org/10.1177/0013164419897307,verified_primary,2026-09-20 +L10,Heinz et al. Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries,2022,peer_reviewed,cross-national psychometric application,Eight-item adolescent HBSC symptom checklist,"229,906 adolescents across 46 countries",Configural/metric invariance was more defensible than scalar invariance; alignment identified item non-invariance,Demonstrates the scale of cross-country adolescent DIF and consequences for comparisons,"Uses one established common instrument, not different survey tools or unseen-item semantic prediction",Cross-national adolescent comparisons require item-level invariance/DIF checks,A common questionnaire automatically yields scalar comparability,10.1186/s12874-022-01698-3,https://doi.org/10.1186/s12874-022-01698-3,verified_primary,2026-09-20 +L11,Savitsky and Williams. Pseudo Bayesian Mixed Models under Informative Sampling,2022,peer_reviewed,"method, simulation and application",Hierarchical models under informative multistage sampling,Simulation plus business-establishment survey example,Weighting only unit likelihood contributions can remain biased when random effects correlate with design; weighting random-effect distributions addresses this setting,Constrains how hierarchical country/survey effects and design weights can be combined,Not an IRT application; requires inclusion-probability information and design assumptions,Complex-sample Bayesian multilevel models need design-aware treatment beyond naive weighted likelihood,Multiplying every likelihood by a weight guarantees correct interval coverage,10.2478/jos-2022-0039,https://doi.org/10.2478/jos-2022-0039,verified_primary,2026-09-20 +L12,Wu and Stephenson. Bayesian estimation methods for survey data with potential applications to health disparities research,2024,peer_reviewed_review,narrative methodological review,Bayesian analysis of complex survey data,"Reviews MRP, weighted pseudo-likelihood and synthetic-population approaches",No single Bayesian survey method is universally sufficient; assumptions and target estimands determine the route,Provides the survey-design method map for later model specifications and sensitivity analyses,Review rather than project-specific validation; does not resolve IRT identification,Multiple defensible Bayesian survey strategies exist and must be chosen by estimand/design,Bayesian modeling automatically corrects informative sampling,10.1002/wics.1633,https://doi.org/10.1002/wics.1633,verified_primary,2026-09-20 +L13,Wu et al. Statistical harmonization of versions of measures across studies using external data,2025,peer_reviewed,calibration-sample method,Self-rated health and memory measured with different response formats,External calibration sample of 300 participants,A bridge sample answering both versions enabled model-based statistical harmonization with moderate agreement,Shows why bridge data may be necessary when archival surveys lack empirical links,Different constructs and older clinical population; external sample design differs from multi-item IRT,Targeted bridge data can identify transformations unavailable from disconnected archives,Text similarity can replace empirical bridge data without uncertainty,10.1016/j.annepidem.2025.01.002,https://doi.org/10.1016/j.annepidem.2025.01.002,verified_primary,2026-09-20 diff --git a/research/protocol/literature_matrix.xlsx b/research/protocol/literature_matrix.xlsx new file mode 100644 index 0000000..df3105e Binary files /dev/null and b/research/protocol/literature_matrix.xlsx differ diff --git a/research/protocol/literature_matrix.xlsx.inspect.ndjson b/research/protocol/literature_matrix.xlsx.inspect.ndjson new file mode 100644 index 0000000..b0ea6c9 --- /dev/null +++ b/research/protocol/literature_matrix.xlsx.inspect.ndjson @@ -0,0 +1,230 @@ +{"kind":"workbook","id":"wb/gycqr5","sheets":1,"tables":1} +{"kind":"sheet","id":"ws/dub4q8","name":"Literature Matrix","index":0,"range":"A1:P17","address":"A1:P17","tables":1} +{"kind":"table","sheet":"Literature Matrix","address":"A1:P17","rows":17,"cols":16,"values":[["Focused literature matrix",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],["Questionnaire harmonization, semantic item models, cross-national DIF, and complex-survey Bayesian inference. Checked 2026-09-20.",null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],[null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null],["source_id","citation","year","publication_status","evidence_type","domain_and_data","sample_or_scope","main_result","project_use","limitations","supports","does_not_support","doi_or_id","source_url","verification_status","checked_date"],["L01","Chen et al. Pre-statistical harmonization of behavioral instruments across eight surveys and trials",2021,"peer_reviewed","empirical workflow/methods","Behavioral instruments across eight dementia surveys and trials","Eight studies; manual instrument review plus automated raw-data checks","Comparable-looking items often differed in wording, response options, scoring, or direction and required pre-statistical review","Defines the source-review and crosswalk work required before statistical linking","Different population and constructs; does not test semantic embeddings or cross-national DIF","Official wording, response options, scoring, and populations must be reviewed before pooling","Semantic similarity alone establishes psychometric equivalence","10.1186/s12874-021-01431-6","https://doi.org/10.1186/s12874-021-01431-6","verified_primary","2026-09-20"],["L02","Kołczyńska. Combining multiple survey sources: A reproducible workflow and toolbox for survey data harmonization",2022,"peer_reviewed","methods/workflow","Four cross-national survey projects; trust items","ESS, EVS, EQLS and Eurobarometer example","Crosswalk-centered, human-auditable documentation improves reproducibility of ex-post harmonization","Supports machine-readable source crosswalks, recodes, provenance and status tracking","Focuses recoding/documentation rather than latent linking or item semantics","Harmonization decisions and transformations need reusable documentation","A documented crosswalk proves measurement invariance","10.1177/20597991221077923","https://doi.org/10.1177/20597991221077923","verified_primary","2026-09-20"],["L03","McElroy et al. Using natural language processing to facilitate the harmonisation of mental health questionnaires",2024,"peer_reviewed","empirical validation","Five mental-health questionnaires in a UK adult sample","2,058 participants; 741 item pairs","Sentence-BERT semantic similarity correlated moderately with empirical item correlations and predicted held-out pair correlations with small error","Closest evidence for semantic item matching and response-structure signal","Adult UK sample, overlapping questionnaires and shared respondents; manual rules still needed; no cross-country DIF or survey-design inference","Text embeddings can help propose harmonization candidates","Embedding similarity verifies psychometric equivalence or transportability","10.1186/s12888-024-05954-2","https://doi.org/10.1186/s12888-024-05954-2","verified_primary","2026-09-20"],["L04","Ravenda et al. Rethinking psychometrics through LLMs: how item semantics shape measurement and prediction in psychological questionnaires",2025,"peer_reviewed","empirical proof-of-concept","Big Five, DASS-42, GAD-7 and PHQ-9 questionnaire data","Large public questionnaire datasets; proof-of-concept response prediction","Semantic structure predicted empirical correlation patterns and supported prediction of responses to unseen items","Shows semantic representations can encode response-structure information in psychological questionnaires","Cross-cultural and multilingual transport were not established; predictive proof-of-concept is not survey harmonization","Item semantics may explain part of response covariance","Universal psychometric equivalence, DIF recovery or calibrated cross-national latent scores","10.1038/s41598-025-21289-8","https://doi.org/10.1038/s41598-025-21289-8","verified_primary","2026-09-20"],["L05","Yancey et al. BERT-IRT: Accelerating Item Piloting with BERT Embeddings and Explainable IRT Models",2024,"peer_reviewed_conference","method plus operational evaluation","Duolingo English Test items","High-stakes language assessment item bank; exact proprietary sample details require full-paper extraction","BERT embeddings and engineered features reduced pilot length while maintaining reported criterion validity and reliability","Direct precedent for text features predicting IRT item parameters","Educational test items differ from suicide-related survey items; does not address cross-country DIF, complex samples or latent phenotype harmonization","Text-derived item features can inform item-parameter estimation","This project's core method is unprecedented or immediately transferable to health surveys","ACL Anthology 2024.bea-1.35","https://aclanthology.org/2024.bea-1.35/","verified_primary","2026-09-20"],["L06","Chen and Chen. From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings",2026,"preprint","method/benchmark","Mathematics and medical-licensure item banks","Two item banks; repeated cross-validation and simulation-based ceilings","Difficulty was more predictable than other parameters; reliability/design ceilings and repeated splits changed interpretation","Requires uncertainty-aware targets, repeated grouped validation and ceiling analysis for semantic parameter prediction","Preprint; educational/assessment domains; not cross-national mental-health surveys","Parameter-prediction benchmarks need target reliability and design ceilings","Reported RMSE alone establishes useful semantic signal","arXiv:2607.07141","https://arxiv.org/abs/2607.07141","verified_preprint","2026-09-20"],["L07","Peters et al. Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review",2025,"preprint","systematic review","Automated item-difficulty prediction","37 articles through May 2025","Language models can predict item difficulty in some settings, but studies vary in datasets, splits, targets and metrics","Maps existing text-to-difficulty literature and prevents novelty overclaiming","Preprint; focuses large-scale assessment rather than health questionnaires, DIF or survey design","Text-based difficulty prediction is an established research area","Reported best-case metrics transfer to this project","arXiv:2509.23486","https://arxiv.org/abs/2509.23486","verified_preprint","2026-09-20"],["L08","Muthén and Asparouhov. IRT studies of many groups: the alignment method",2014,"peer_reviewed","method plus Monte Carlo","Binary knowledge items across many country groups","Two surveys plus simulation","Alignment estimates group factor means/variances without requiring exact invariance and reports parameter non-invariance","Core comparator for many-country measurement invariance and DIF","Requires a prespecified factor structure and adequate linkage; alignment is not proof that all groups share one construct","Approximate invariance can be studied across many groups","Alignment repairs absent empirical connections or identifies a scale from semantics alone","10.3389/fpsyg.2014.00978","https://doi.org/10.3389/fpsyg.2014.00978","verified_primary","2026-09-20"],["L09","Mansolf et al. Extensions of Multiple-Group Item Response Theory Alignment",2020,"peer_reviewed","method, simulation and application","International psychiatric genomics consortium with disparate item sets and formats","Multiple sites/instruments plus real-data-based simulation","Extended alignment accommodated differing item sets and response categories and recovered parameters in simulation","Closest latent-harmonization comparator for psychiatric phenotypes with nonidentical instruments","Needs specified construct/factor model and empirical connections; population and sampling designs differ from this project","Disparate psychiatric item sets can sometimes be aligned with explicit assumptions","Semantic priors alone create a common scale or eliminate anchor requirements","10.1177/0013164419897307","https://doi.org/10.1177/0013164419897307","verified_primary","2026-09-20"],["L10","Heinz et al. Item response theory and differential test functioning analysis of the HBSC-Symptom-Checklist across 46 countries",2022,"peer_reviewed","cross-national psychometric application","Eight-item adolescent HBSC symptom checklist","229,906 adolescents across 46 countries","Configural/metric invariance was more defensible than scalar invariance; alignment identified item non-invariance","Demonstrates the scale of cross-country adolescent DIF and consequences for comparisons","Uses one established common instrument, not different survey tools or unseen-item semantic prediction","Cross-national adolescent comparisons require item-level invariance/DIF checks","A common questionnaire automatically yields scalar comparability","10.1186/s12874-022-01698-3","https://doi.org/10.1186/s12874-022-01698-3","verified_primary","2026-09-20"],["L11","Savitsky and Williams. Pseudo Bayesian Mixed Models under Informative Sampling",2022,"peer_reviewed","method, simulation and application","Hierarchical models under informative multistage sampling","Simulation plus business-establishment survey example","Weighting only unit likelihood contributions can remain biased when random effects correlate with design; weighting random-effect distributions addresses this setting","Constrains how hierarchical country/survey effects and design weights can be combined","Not an IRT application; requires inclusion-probability information and design assumptions","Complex-sample Bayesian multilevel models need design-aware treatment beyond naive weighted likelihood","Multiplying every likelihood by a weight guarantees correct interval coverage","10.2478/jos-2022-0039","https://doi.org/10.2478/jos-2022-0039","verified_primary","2026-09-20"],["L12","Wu and Stephenson. Bayesian estimation methods for survey data with potential applications to health disparities research",2024,"peer_reviewed_review","narrative methodological review","Bayesian analysis of complex survey data","Reviews MRP, weighted pseudo-likelihood and synthetic-population approaches","No single Bayesian survey method is universally sufficient; assumptions and target estimands determine the route","Provides the survey-design method map for later model specifications and sensitivity analyses","Review rather than project-specific validation; does not resolve IRT identification","Multiple defensible Bayesian survey strategies exist and must be chosen by estimand/design","Bayesian modeling automatically corrects informative sampling","10.1002/wics.1633","https://doi.org/10.1002/wics.1633","verified_primary","2026-09-20"],["L13","Wu et al. 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NLP/LLM 题目表示是否已经用于问卷匹配或响应结构预测？ +3. 文本特征是否已经用于预测 IRT 题目参数？ +4. 多组 IRT、alignment 和 DIF 如何处理多国家或不同题集？ +5. Bayesian 层级模型如何处理复杂和信息性抽样？ + +检索优先使用期刊页面、PubMed/PMC、ACL Anthology 和论文预印本原页。无法独立确认存在或元数据的来源不进入矩阵。同行评审与预印本分开标记。 + +## 证据综合 + +### 1. 数据协调必须先做题目和来源审计 + +Chen 等和 Kołczyńska 的工作支持先核对研究总体、题目正文、回答选项、计分方向、版本及转换记录。它们不支持仅凭相似变量名合并，也不支持把有文档的 crosswalk 当作测量等价证据。 + +### 2. 语义问卷协调不是空白领域 + +McElroy 等已用 Sentence-BERT 比较心理健康问卷题目，并在共同作答样本中检验语义相似与实证相关。Ravenda 等进一步展示题目语义与问卷响应结构及 unseen-item 响应预测的关系。因此，本项目不能宣称首次把语言模型用于心理问卷结构或协调。 + +### 3. 文本预测 IRT 参数已有直接先例 + +BERT-IRT 已把 BERT 嵌入和工程特征用于题目参数估计。Chen 与 Chen 的预印本及 Peters 等的预印本综述进一步表明 text-to-parameter / item-difficulty modeling 已形成独立研究线，并提示目标可靠性上限、重复交叉验证和 scale-free 指标的重要性。因此，“让嵌入预测题目难度”本身不是充分创新。 + +### 4. 多组、跨国和不同题集协调已有成熟比较对象 + +Muthén 与 Asparouhov 的 alignment、Mansolf 等对不同题集/回答格式的扩展，以及 Heinz 等对 46 国青少年量表的分析，构成项目必须比较或讨论的方法基础。共同问卷也可能不满足 scalar invariance；不同题集的 alignment 仍需要预设构念结构和经验连接。 + +### 5. 复杂抽样不能用简单加权似然一句带过 + +Savitsky 与 Williams 表明，在多层信息性抽样下，只给个体 likelihood 加权仍可能不足。Wu 与 Stephenson 的综述也显示 MRP、pseudo-likelihood 和 synthetic population 各自依赖不同估计目标和设计信息。本项目必须把设计型描述性估计、模型内权重处理和设计一致的区间/重抽样检查分开。 + +## 当前可辩护的贡献候选 + +现有矩阵没有发现一项已核验工作同时完成以下组合： + +- 以官方题目、回答标签、时间窗口和真实复杂抽样回答建立跨 GSHS、YRBS、NSDUH 的可追溯题库； +- 在题目家族级留出下，让多语言语义表示条件化阈值、区分度和受约束 DIF； +- 同时评估未见题目、未见问卷、未见国家/地区和时间外迁移； +- 对未见题目/国家效应传播不确定性，而不是设为零； +- 与直接协调、传统/层级 IRT、人工标签和非语义模型做公平消融； +- 明确处理权重、PSU、stratum 及设计型不确定性。 + +这只是阶段 0 的候选贡献边界，不是首创证明。正式论文前仍需扩大检索、做引用追踪和更新检索。 + +## 对研究设计的约束 + +1. 语义只提供候选先验或预测信息，不能替代经验锚定与可识别性。 +2. 训练/测试必须按题目家族和外层域分组；单次随机划分和 RMSE 不足以支持新题目校准。 +3. 对经验题目参数进行监督学习时，需传播参数估计误差或使用后验样本。 +4. 若跨调查图不连通，应提出桥接样本或限制主张，不用语义相似伪造共同标度。 +5. alignment 和其他传统协调方法是必要基线，不是可省略的背景文献。 +6. 复杂抽样区间需独立验证；Bayesian 标签本身不保证设计一致性。 + +## 矩阵构成 + +- 总条目：13 +- 同行评审：11 +- 预印本：2 +- 每条均记录：证据类型、数据/样本、主要结果、项目用途、限制、允许支持与不允许支持的主张、DOI/永久链接和核验状态。 + diff --git a/research/protocol/project_charter.md b/research/protocol/project_charter.md new file mode 100644 index 0000000..e525c24 --- /dev/null +++ b/research/protocol/project_charter.md @@ -0,0 +1,138 @@ +# Language-Conditioned Psychometric Harmonization Model：项目章程 + +版本：0.1.0 +冻结日期：2026-09-20 +状态：`passed_for_gate_0` +适用范围：阶段 0–2；阶段 2 完成后依据可识别性证据修订 + +## 1. 项目目的与主要研究问题 + +本项目不是普通个体风险预测竞赛，也不预设所有自杀相关变量属于一个单维量表。项目首先确认数据结构支持 IRT、层级测量、阶段/多结局模型还是只能直接协调。 + +**Primary RQ：** 在 GSHS、YRBS 与 NSDUH 青少年相关数据中，题目正文、时间窗口和回答标签的语义表示，能否相对于最佳适用的无文本层级测量基线和人工构念标签基线，提高未见题目家族、未见问卷版本及未见国家/工具上的测量预测与校准，同时不造成有实质意义的区间质量退化？ + +支撑问题： + +1. 现有调查的题目共现、锚定连接和题目家族多样性是否足以识别各构念？ +2. 措辞、时间窗口、回答格式、语言、国家、年份和工具是否产生有实质意义的 DIF 或测量非等价？ +3. 题目语义能否解释训练资料中的响应结构和题目参数，并跨题目家族泛化？ +4. 对未见国家、工具和题目，模型的不确定性是否校准；zero-shot 与 few-shot 的表现如何？ +5. 复杂抽样、缺失/跳题、锚题和总体定义的合理替代方案是否改变结论？ + +## 2. 可证伪假设 + +语言条件化测量模型在预先冻结的外层迁移任务中，相对于最佳适用的 M0/M1/M2 与人工标签基线，在指定共同主指标上达到预设最小有意义改善，且校准或覆盖不劣于预设界限。 + +改善阈值、非劣界限和主指标数值将在阶段 2 的信息量审计和阶段 3 的模拟/功效校准后冻结。若语义模型没有稳定增益，项目输出应是阴性 benchmark 或适用边界，不更换测试集或事后挑选指标。 + +## 3. 目标总体和时间地域范围 + +- 对象：青少年调查参与者。 +- GSHS：跨国开发候选，主要是学校在读样本；常见核心年龄为 13–17 岁，但必须逐国家—年份组件核验。 +- YRBS：问卷和时间迁移候选；美国高中生样本不得外推到全部美国青少年。 +- NSDUH：外部候选；家庭抽样框与学校调查框不可静默视为同一总体。 +- 跨工具比较优先使用年龄与适用总体共同支持域。精确年龄带在阶段 1 建立总体适用表后冻结；不能统一的总体分层报告。 +- YRBS 原始年份候选为 1991–2023；当前处理入口只到 2019，2021/2023 在布局验证前隔离。 +- NSDUH 当前处理候选为 2021–2024，只纳入确认属于青少年模块且有官方问题/编码证据的变量。 +- 国家背景仅在测量层建立后加入，并按调查时点可得性匹配。 + +## 4. 构念与变量角色 + +测量层分开登记，不预设单维： + +1. suicidal ideation +2. suicide plan +3. suicide attempt（二元和次数形式分开） +4. self-harm（是否共模需证据） +5. sadness/hopelessness（是否共模需证据） + +欺凌、孤独、睡眠、物质使用、家庭/同伴支持、暴力暴露及国家背景属于解释或分层变量，不因相关或预测作用自动变成自杀风险量表题目。横断面关联不表述为因果或个体纵向预测。 + +阶段 2 为每个构念选择：IRT/多维测量、阶段/潜类别/多结局、直接二分类/频数协调，或分调查报告。 + +## 5. 数据角色与唯一候选入口 + +| 数据源 | 候选角色 | 唯一候选入口 | 状态 | +|---|---|---|---| +| GSHS | 跨国开发及未见国家验证 | `Dataset/GSHS-全球学生健康调查数据/GSHS/01_data/GSHS.csv` | `audit_required` | +| YRBS | 问卷版本及时间迁移 | 处理包 `YRBS/full_by_year`（1991–2019）；原始核验用 `YRBS_National_1991_2023/raw` | `conditional` | +| NSDUH | 外部工具/总体候选 | `可直接分析数据包_NSDUH_YRBS/NSDUH/nsduh_2021_2024_full.parquet` | `conditional` | +| World Bank/WHO context | 后期国家—年份解释层 | `WHO-country_context/world_bank_country_year_1990_2025.csv` 及来源长表 | `conditional` | +| 其他目录 | 外部候选、文档或 provenance archive | 无 | 不进入核心分析，除非书面改版 | + +入口是审计入口，不表示其中变量已经通过题义、总体、设计或许可审计。 + +## 6. 冻结的迁移任务 + +1. **Unseen item family**：同一家族全部措辞、翻译、回答和监督信号留出。 +2. **Unseen questionnaire version**：整个版本及泄漏等价版本留出。 +3. **Unseen country**：该国全部年份和组件回答留出，未知国家效应从层级分布预测并传播不确定性。 +4. **Unseen region**：整个预定地区留出。 +5. **Temporal extrapolation**：早期训练、后期测试，背景按预测时点可得性处理。 +6. **Cross-instrument**：总体和结局定义审计通过后再跨工具评估。 + +随机个体划分只用于调试或辅助比较，不作为核心迁移主张证据。 + +## 7. 估计目标和指标层级 + +主要目标是题目阈值/难度、区分度、DIF、共同标度成立时的潜在表型/群体分布，以及新题目/新域的预测分布与不确定性。 + +共同主指标候选：未见题目家族 held-out log score；未见国家/工具共同目标上的校准或分布误差；模拟已知真值下参数恢复与覆盖；群体估计的设计加权误差与区间表现。AUPRC、AUROC、Brier 等为辅助预测指标，不能单独证明测量协调成功。 + +## 8. 识别和调查设计原则 + +- 语义相似只产生候选连接，不证明锚题不变或共同标度成立。 +- 国家均值、阈值平移和无约束 DIF 可能混淆；正式模型必须声明标度、参考组、锚题和 DIF 约束。 +- 独立信息单位主要是题目家族/版本及国家/工具等外层单元；大量受访者不能补偿题目种类不足。 +- 保留权重、PSU、stratum、国家—年份及组件标识。 +- 权重进入似然不等同于设计一致区间；描述性设计方差、模型内权重和设计型重抽样分开验证。 +- 未施测、不适用、合法跳题、拒答、不知道、普通缺失和真实阴性分开。 + +## 9. 范围外事项 + +- 临床诊断、个体自杀预测或资源分配工具。 +- 用国家自杀死亡率作个体结局。 +- 无总体限定地把学校样本外推到全部青少年。 +- 在跨构念/数据集/任务证据不足时称为 foundation model。 +- 在阶段 2/3 前冻结最终深度架构、最终嵌入模型或结果后阈值。 +- 把 AI 预审登记为两名人类审核者。 +- 分发未经确认允许的微观数据。 + +## 10. 伦理、许可和治理 + +- 数据按去标识化二次数据处理，但使用仍受来源现行条款和机构要求约束。 +- 不重新识别参与者，不公开再分发许可未确认的微数据。 +- 主要结局进入确认性分析前需两名实际审核者复核；AI 检查只标 provisional。 +- 输出记录 AI 辅助、数据来源、版本、哈希、模型/包版本及 unknown 状态。 +- 原始数据只读；衍生产物写入 `research/`。 +- 改变总体、构念、数据角色、测试任务或成功判据时，必须提高本章程版本并在 `Progression.md` 记录原因。 + +## 11. 阶段 0 待验证清单 + +1. 各调查组件的年龄、在校状态、地域、语言和问卷版本。 +2. 主要结局官方正文、答案标签、时间窗口、跳题和派生规则。 +3. GSHS 权重、PSU、stratum 的调查级有效性。 +4. YRBS 2021/2023 官方布局、导入程序、记录数和本地文件一致性。 +5. NSDUH 青少年/成人/COVID/派生变量边界及正确权重/方差设计。 +6. 锚题图、题目共现、阳性事件、结构缺失及有效题目家族数。 +7. 各来源现行许可、引用要求及衍生数据分发范围。 +8. 最接近方法文献和真实贡献边界；不得预设首创。 +9. 缺失的 R、复杂抽样、PyMC/ArviZ 和心理测量环境的锁定方案。 + +## 12. Gate 与停止条件 + +Gate 0 要求本章程、数据清单、文献矩阵和环境审计齐备。Gate 1 要求正式题目均有来源、编码、总体与缺失规则且主要结局完成人工双重核查。Gate 2 要求每个构念得到可进入潜变量模型/有条件/仅直接协调/不纳入的证据判定。 + +官方文本或编码无法确认、图不连通/标度不可识别、题目家族不足、设计字段不完整或参数恢复失败时按工作流降级；不得通过增加模型复杂度、替换最终测试或扩大主张掩盖失败。 + +## 13. 输入绑定 + +| 输入 | SHA-256 | +|---|---| +| `Document/research-workflow.md` | `394a191359298132fc2a56304dbc49b5934f47af22d2e0fff47d28a0d9edcaeb` | +| `Document/项目的正式定位.md` | `28a415ff5d42649df2a7c68079ae0abfd295cbffb5fead216934a895cca4cd13` | +| `Document/language-conditioned-psychometric-harmonization-model.md` | `39d2050735f4df28c6abe08f8d1de6094b7ac11d0b6f2ccf9fd66d4cc450a825` | +| `research/audit/data_manifest.csv` | `0427a19c3287a2691edbd9a4486917989fe369fcb0f729a01477ce27cd8f98f2` | +| `research/audit/data_source_registry.csv` | `ac68b9928f8855c5373034a30adda316da1da2f1a1e890b0c23f836f12d55b67` | +| `research/audit/environment_audit.md` | `c84a6b05718bbaf7762021aa45b2f983e7c9ef844d41592d2138439251e929eb` | +