{"type":"atis-latch","atis":"","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:38:41.075Z","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","content":"Begin"}
{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:38:41.082Z","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"parentUuid":null,"isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":"Begin"},"uuid":"235db49c-05c9-4eda-aade-6d8df4a005b1","timestamp":"2026-10-30T05:38:41.086Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-shape","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","timestamp":"2026-10-30T05:38:41.100Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RT0018\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"da3015be-1bde-4e3c-b2b9-70fa56499a4f","timestamp":"2026-10-30T05:38:41.100Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"174514742e598c47f4ced14da4e742d29a88a990f1cbebed58e8f2ccbb1e5ca3"}
{"parentUuid":"235db49c-05c9-4eda-aade-6d8df4a005b1","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"5a9d857b-d1bf-486e-8511-23170b464c53","timestamp":"2026-10-30T05:38:41.091Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"5a9d857b-d1bf-486e-8511-23170b464c53","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"b881dcd5-ea68-4448-8617-8bcfe87e5bc7","timestamp":"2026-10-30T05:38:41.091Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"b881dcd5-ea68-4448-8617-8bcfe87e5bc7","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"d2731f50-20a8-41ea-b719-23756dce5bff","timestamp":"2026-10-30T05:38:41.092Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"tools-used","tools":["Bash"],"sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"parentUuid":"d2731f50-20a8-41ea-b719-23756dce5bff","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtAeMhnCXTr2XVqzZkF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017srLpjfCDEe2SMyWb4P8YA","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":730,"cache_read_input_tokens":1504,"output_tokens":63,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":730},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXtAe56Qgoz7vX6hZxCM","requestRef":"da3015be-1bde-4e3c-b2b9-70fa56499a4f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a2a61f4-f88b-42c6-821e-01d300c5eaa4","timestamp":"2026-10-30T05:38:42.415Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"6a2a61f4-f88b-42c6-821e-01d300c5eaa4","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017srLpjfCDEe2SMyWb4P8YA","type":"tool_result","content":"# T2-simulation — Finite-sample performance of three cluster-robust confidence intervals for a mean: a simulation study\n\n## Scientific question\nWhen observations are grouped in a small number of clusters, how reliable are the usual cluster-robust confidence intervals for a population mean? This is a complete simulation-methodology project: design → implementation → Monte Carlo evaluation with its own uncertainty → identification of failure regimes → figure and table → an executable technical note. The estimand of the inferential procedures is the population mean μ of y, estimated by the sample mean ȳ of N clustered observations; the estimands of the study are, for each of the nine design cells (G ∈ {5, 10, 30} clusters × intra-cluster correlation ICC ∈ {0.0, 0.2, 0.5}) and each of three interval procedures, (a) the actual coverage probability of the nominal 95% interval, (b) for the two sandwich estimators, the relative bias of the variance estimator, E[V̂] / Var(ȳ), where Var(ȳ) is the exact variance of the sample mean under the design, (c) the Monte Carlo standard error of each coverage estimate, and (d) the set of (procedure, cell) pairs whose coverage falls below 0.90 — the failure regimes.\n\n## Inputs\n- `data/design.csv` — simulated-data design written by the task designer (nothing here is observed data): one row per cell with `cell_id`, `G`, `icc`, `cluster_sizes` (the G cluster sizes separated by `|`: the pattern 8, 16, 32, 64 repeated), `N`, `R` = 2000 replications, `B` = 399 bootstrap draws, `seed`, `mu` = 0, `total_variance` = 1, `nominal_level` = 0.95. See `data/README.md`.\n\n## Information-availability rules\n- Data-generating process (per replication): y_ig = μ + u_g + e_ig with u_g ~ N(0, ICC) i.i.d. across clusters and e_ig ~ N(0, 1 − ICC) i.i.d. across observations, independent of u; cluster g has n_g observations as listed in `cluster_sizes`. Exact variance of the sample mean: Var(ȳ) = [ICC · Σ_g n_g² + (1 − ICC) · N] / N².\n- Interval procedures (all three required, exactly as specified, with ȳ as the point estimate and residuals ê_ig = y_ig − ȳ):\n  - **CR0**: V̂_CR0 = Σ_g (Σ_i ê_ig)² / N²; interval ȳ ± 1.96 · √V̂_CR0;\n  - **CR1**: V̂_CR1 = [G / (G − 1)] · V̂_CR0; interval ȳ ± t_{0.975, G−1} · √V̂_CR1 (Student t with G − 1 degrees of freedom);\n  - **WCB** (wild cluster bootstrap-t, unrestricted, Rademacher): for b = 1..B draw cluster weights w_g^b ∈ {−1, +1} with equal probability, form y*_ig = ȳ + w_g^b · ê_ig, compute ȳ* and its V̂_CR1* on the bootstrap sample, t*_b = (ȳ* − ȳ) / √V̂_CR1*; critical value q = the 0.95 empirical quantile of |t*_1|, …, |t*_B|; interval ȳ ± q · √V̂_CR1.\n- Use R = 2000 replications per cell, B = 399 bootstrap draws per replication and the cell's seed from `design.csv`; do not change the grid, R or B. A procedure's coverage in a cell is the fraction of the R intervals that contain μ. The Monte Carlo standard error of a coverage estimate p̂ is √(p̂(1 − p̂)/R).\n- A (procedure, cell) pair is a **failure regime** when its estimated coverage is below 0.90. A pair is **consistent with nominal coverage** when |p̂ − 0.95| ≤ 2 · MC SE.\n\n## Required outputs\n- `out/table1.csv` — 27 rows (3 procedures × 9 cells) with columns `estimator` (`cr0`, `cr1`, `wcb`), `cell_id`, `G`, `icc`, `coverage`, `mc_se_coverage`, `rel_bias_var` (empty for `wcb`), `mean_ci_halfwidth`, `R`.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T2-simulation\" |\n| `R` | integer | — | replications per cell actually used |\n| `B` | integer | — | bootstrap draws per replication actually used |\n| `n_cells` | integer | — | number of design cells simulated |\n| `coverage` | object | — | {\"<estimator>_G<G>_icc<icc>\": number} for the 27 pairs, e.g. `\"cr1_G5_icc0.2\"` (icc written as 0.0, 0.2, 0.5) |\n| `mc_se_coverage` | object | — | same keys: Monte Carlo SE of each coverage estimate |\n| `rel_bias_var` | object | — | 18 keys (cr0 and cr1 only): E[V̂]/Var(ȳ) estimated by the Monte Carlo mean |\n| `failure_cells` | array of strings | — | the pairs with coverage < 0.90, same key format, any order |\n| `n_failure_cells` | integer | — | length of `failure_cells` |\n| `nominal_cells` | array of strings | — | the pairs whose coverage is consistent with nominal (|p̂ − 0.95| ≤ 2 MC SE) |\n| `best_procedure_small_G` | string | — | the procedure with the highest average coverage over the three G = 5 cells |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the design or during the simulation and how handled; empty list if none |\n| `n_input_rows` | object | rows | {\"design.csv\": number of data rows} |\n| `n_analysed` | integer or null | — | total number of replications simulated (cells × R) |\n| `random_seed` | integer or null | — | the seed of cell C1 (the other seeds are in `design.csv`) |\n\n- `out/figure1.png` — coverage against G for the three procedures, one panel per ICC (or an equivalent figure), with the nominal level and the 0.90 failure line marked.\n- `out/report.md` — an executable technical note with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must discuss the Monte Carlo error of the coverage and bias estimates; \"Limitations and open issues\" must state what the design does not cover (distributions, heterogeneity, regressors, other weights).\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/design.csv` only, deterministically (same seeds → same numbers).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Vectorise the bootstrap (a B × G weight matrix per replication is enough); the full grid runs in a few minutes in numpy. The analysis must complete within 10 minutes.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ndesign.csv\nREADME.md","is_error":false}]},"uuid":"c4b8f18e-5cab-49cd-aed0-4a23c15f91c1","timestamp":"2026-10-30T05:38:42.491Z","toolUseResult":{"stdout":"# T2-simulation — Finite-sample performance of three cluster-robust confidence intervals for a mean: a simulation study\n\n## Scientific question\nWhen observations are grouped in a small number of clusters, how reliable are the usual cluster-robust confidence intervals for a population mean? This is a complete simulation-methodology project: design → implementation → Monte Carlo evaluation with its own uncertainty → identification of failure regimes → figure and table → an executable technical note. The estimand of the inferential procedures is the population mean μ of y, estimated by the sample mean ȳ of N clustered observations; the estimands of the study are, for each of the nine design cells (G ∈ {5, 10, 30} clusters × intra-cluster correlation ICC ∈ {0.0, 0.2, 0.5}) and each of three interval procedures, (a) the actual coverage probability of the nominal 95% interval, (b) for the two sandwich estimators, the relative bias of the variance estimator, E[V̂] / Var(ȳ), where Var(ȳ) is the exact variance of the sample mean under the design, (c) the Monte Carlo standard error of each coverage estimate, and (d) the set of (procedure, cell) pairs whose coverage falls below 0.90 — the failure regimes.\n\n## Inputs\n- `data/design.csv` — simulated-data design written by the task designer (nothing here is observed data): one row per cell with `cell_id`, `G`, `icc`, `cluster_sizes` (the G cluster sizes separated by `|`: the pattern 8, 16, 32, 64 repeated), `N`, `R` = 2000 replications, `B` = 399 bootstrap draws, `seed`, `mu` = 0, `total_variance` = 1, `nominal_level` = 0.95. See `data/README.md`.\n\n## Information-availability rules\n- Data-generating process (per replication): y_ig = μ + u_g + e_ig with u_g ~ N(0, ICC) i.i.d. across clusters and e_ig ~ N(0, 1 − ICC) i.i.d. across observations, independent of u; cluster g has n_g observations as listed in `cluster_sizes`. Exact variance of the sample mean: Var(ȳ) = [ICC · Σ_g n_g² + (1 − ICC) · N] / N².\n- Interval procedures (all three required, exactly as specified, with ȳ as the point estimate and residuals ê_ig = y_ig − ȳ):\n  - **CR0**: V̂_CR0 = Σ_g (Σ_i ê_ig)² / N²; interval ȳ ± 1.96 · √V̂_CR0;\n  - **CR1**: V̂_CR1 = [G / (G − 1)] · V̂_CR0; interval ȳ ± t_{0.975, G−1} · √V̂_CR1 (Student t with G − 1 degrees of freedom);\n  - **WCB** (wild cluster bootstrap-t, unrestricted, Rademacher): for b = 1..B draw cluster weights w_g^b ∈ {−1, +1} with equal probability, form y*_ig = ȳ + w_g^b · ê_ig, compute ȳ* and its V̂_CR1* on the bootstrap sample, t*_b = (ȳ* − ȳ) / √V̂_CR1*; critical value q = the 0.95 empirical quantile of |t*_1|, …, |t*_B|; interval ȳ ± q · √V̂_CR1.\n- Use R = 2000 replications per cell, B = 399 bootstrap draws per replication and the cell's seed from `design.csv`; do not change the grid, R or B. A procedure's coverage in a cell is the fraction of the R intervals that contain μ. The Monte Carlo standard error of a coverage estimate p̂ is √(p̂(1 − p̂)/R).\n- A (procedure, cell) pair is a **failure regime** when its estimated coverage is below 0.90. A pair is **consistent with nominal coverage** when |p̂ − 0.95| ≤ 2 · MC SE.\n\n## Required outputs\n- `out/table1.csv` — 27 rows (3 procedures × 9 cells) with columns `estimator` (`cr0`, `cr1`, `wcb`), `cell_id`, `G`, `icc`, `coverage`, `mc_se_coverage`, `rel_bias_var` (empty for `wcb`), `mean_ci_halfwidth`, `R`.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T2-simulation\" |\n| `R` | integer | — | replications per cell actually used |\n| `B` | integer | — | bootstrap draws per replication actually used |\n| `n_cells` | integer | — | number of design cells simulated |\n| `coverage` | object | — | {\"<estimator>_G<G>_icc<icc>\": number} for the 27 pairs, e.g. `\"cr1_G5_icc0.2\"` (icc written as 0.0, 0.2, 0.5) |\n| `mc_se_coverage` | object | — | same keys: Monte Carlo SE of each coverage estimate |\n| `rel_bias_var` | object | — | 18 keys (cr0 and cr1 only): E[V̂]/Var(ȳ) estimated by the Monte Carlo mean |\n| `failure_cells` | array of strings | — | the pairs with coverage < 0.90, same key format, any order |\n| `n_failure_cells` | integer | — | length of `failure_cells` |\n| `nominal_cells` | array of strings | — | the pairs whose coverage is consistent with nominal (|p̂ − 0.95| ≤ 2 MC SE) |\n| `best_procedure_small_G` | string | — | the procedure with the highest average coverage over the three G = 5 cells |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | problems detected in the design or during the simulation and how handled; empty list if none |\n| `n_input_rows` | object | rows | {\"design.csv\": number of data rows} |\n| `n_analysed` | integer or null | — | total number of replications simulated (cells × R) |\n| `random_seed` | integer or null | — | the seed of cell C1 (the other seeds are in `design.csv`) |\n\n- `out/figure1.png` — coverage against G for the three procedures, one panel per ICC (or an equivalent figure), with the nominal level and the 0.90 failure line marked.\n- `out/report.md` — an executable technical note with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. \"Uncertainty\" must discuss the Monte Carlo error of the coverage and bias estimates; \"Limitations and open issues\" must state what the design does not cover (distributions, heterogeneity, regressors, other weights).\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/table1.csv` and `out/figure1.png` from `data/design.csv` only, deterministically (same seeds → same numbers).\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Vectorise the bootstrap (a B × G weight matrix per replication is enough); the full grid runs in a few minutes in numpy. The analysis must complete within 10 minutes.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\ndesign.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6a2a61f4-f88b-42c6-821e-01d300c5eaa4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"195ee2a9b9e27acba358012f5b1c75db355b688fa2f4be6b9034e7f882d02c8f","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017srLpjfCDEe2SMyWb4P8YA","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"cacc17a9-0269-49e7-b103-f88a01c3f1a2","timestamp":"2026-10-30T05:38:42.497Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"da3015be-1bde-4e3c-b2b9-70fa56499a4f","keep":2,"tail":["195ee2a9b9e27acba358012f5b1c75db355b688fa2f4be6b9034e7f882d02c8f","80f4acae96565bda3f6098f003d2764f8234cf4f6e5f59c8b553c026372e782c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d03891494bf5fc2d3c09ffe76abb21b0f6d0b3b5f3f32563b3ab86cade349272"}
{"parentUuid":"c4b8f18e-5cab-49cd-aed0-4a23c15f91c1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtAkHPtuSLLBksyYK1u","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RCEzJC7mS3v4pEi39dVLrA","name":"Bash","input":{"command":"cat data/design.csv data/README.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2833,"cache_read_input_tokens":2234,"output_tokens":59,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2833},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXtAjzHnsvZiSchN31xj","requestRef":"cacc17a9-0269-49e7-b103-f88a01c3f1a2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"52b79aed-b7f7-4969-9e9a-24ed94ec20bf","timestamp":"2026-10-30T05:38:43.385Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"52b79aed-b7f7-4969-9e9a-24ed94ec20bf","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RCEzJC7mS3v4pEi39dVLrA","type":"tool_result","content":"cell_id,G,icc,cluster_sizes,N,R,B,seed,mu,total_variance,nominal_level\r\nC1,5,0.0,8|16|32|64|8,128,2000,399,20261101,0.0,1.0,0.95\r\nC2,5,0.2,8|16|32|64|8,128,2000,399,20261102,0.0,1.0,0.95\r\nC3,5,0.5,8|16|32|64|8,128,2000,399,20261103,0.0,1.0,0.95\r\nC4,10,0.0,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261104,0.0,1.0,0.95\r\nC5,10,0.2,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261105,0.0,1.0,0.95\r\nC6,10,0.5,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261106,0.0,1.0,0.95\r\nC7,30,0.0,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261107,0.0,1.0,0.95\r\nC8,30,0.2,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261108,0.0,1.0,0.95\r\nC9,30,0.5,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261109,0.0,1.0,0.95\r\n# Data — T2-simulation\n\nThis project uses **no observed data**. The only input is `design.csv`, written by the task designer: the nine cells of the Monte Carlo design (one row per cell).\n\n| column | meaning |\n|---|---|\n| `cell_id` | C1 … C9 |\n| `G` | number of clusters (5, 10, 30) |\n| `icc` | intra-cluster correlation of the data-generating process (0.0, 0.2, 0.5) |\n| `cluster_sizes` | the G cluster sizes n_1, …, n_G, separated by `|` (the pattern 8, 16, 32, 64 repeated) |\n| `N` | total number of observations in a sample |\n| `R` | number of Monte Carlo replications per cell (2000) |\n| `B` | number of wild-bootstrap draws per replication (399) |\n| `seed` | the seed to use for the cell (numpy `default_rng(seed)` or equivalent) |\n| `mu` | population mean of y (0.0) |\n| `total_variance` | Var(y_ig) = Var(u_g) + Var(e_ig) = 1.0 |\n| `nominal_level` | nominal confidence level (0.95) |\n\nData-generating process for a cell: y_ig = mu + u_g + e_ig, g = 1..G, i = 1..n_g, with u_g ~ N(0, icc) independent across clusters and e_ig ~ N(0, 1 − icc) independent across observations and of u. All simulated data are synthetic; nothing here is observed data.","is_error":false}]},"uuid":"5e6426eb-1ea6-4617-b53d-e96cf5138b23","timestamp":"2026-10-30T05:38:43.410Z","toolUseResult":{"stdout":"cell_id,G,icc,cluster_sizes,N,R,B,seed,mu,total_variance,nominal_level\r\nC1,5,0.0,8|16|32|64|8,128,2000,399,20261101,0.0,1.0,0.95\r\nC2,5,0.2,8|16|32|64|8,128,2000,399,20261102,0.0,1.0,0.95\r\nC3,5,0.5,8|16|32|64|8,128,2000,399,20261103,0.0,1.0,0.95\r\nC4,10,0.0,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261104,0.0,1.0,0.95\r\nC5,10,0.2,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261105,0.0,1.0,0.95\r\nC6,10,0.5,8|16|32|64|8|16|32|64|8|16,264,2000,399,20261106,0.0,1.0,0.95\r\nC7,30,0.0,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261107,0.0,1.0,0.95\r\nC8,30,0.2,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261108,0.0,1.0,0.95\r\nC9,30,0.5,8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16|32|64|8|16,864,2000,399,20261109,0.0,1.0,0.95\r\n# Data — T2-simulation\n\nThis project uses **no observed data**. The only input is `design.csv`, written by the task designer: the nine cells of the Monte Carlo design (one row per cell).\n\n| column | meaning |\n|---|---|\n| `cell_id` | C1 … C9 |\n| `G` | number of clusters (5, 10, 30) |\n| `icc` | intra-cluster correlation of the data-generating process (0.0, 0.2, 0.5) |\n| `cluster_sizes` | the G cluster sizes n_1, …, n_G, separated by `|` (the pattern 8, 16, 32, 64 repeated) |\n| `N` | total number of observations in a sample |\n| `R` | number of Monte Carlo replications per cell (2000) |\n| `B` | number of wild-bootstrap draws per replication (399) |\n| `seed` | the seed to use for the cell (numpy `default_rng(seed)` or equivalent) |\n| `mu` | population mean of y (0.0) |\n| `total_variance` | Var(y_ig) = Var(u_g) + Var(e_ig) = 1.0 |\n| `nominal_level` | nominal confidence level (0.95) |\n\nData-generating process for a cell: y_ig = mu + u_g + e_ig, g = 1..G, i = 1..n_g, with u_g ~ N(0, icc) independent across clusters and e_ig ~ N(0, 1 − icc) independent across observations and of u. All simulated data are synthetic; nothing here is observed data.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"52b79aed-b7f7-4969-9e9a-24ed94ec20bf","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"3beaedf99fce1694df6b507e5da3f37d5b2e7c399df06ceacc0aa1d6198b2d6e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01RCEzJC7mS3v4pEi39dVLrA","name":"Bash","input":{"command":"cat data/design.csv data/README.md"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"14c5fc94-45b7-4384-9529-ba981f7667bb","timestamp":"2026-10-30T05:38:43.413Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"cacc17a9-0269-49e7-b103-f88a01c3f1a2","keep":4,"tail":["3beaedf99fce1694df6b507e5da3f37d5b2e7c399df06ceacc0aa1d6198b2d6e","cfe048983134365546bfd7caf97f203495cdc1ba35dae45c21259d97db8ef4d9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b37f120af38fafae2f349777a1850ca51928eaba197e64e2f23ae1d97db398d1"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5e6426eb-1ea6-4617-b53d-e96cf5138b23","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"type":"atis-latch","atis":"","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"parentUuid":"5e6426eb-1ea6-4617-b53d-e96cf5138b23","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtAp9a1BiTqR9kZHjhn","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1172,"cache_read_input_tokens":5067,"output_tokens":4003,"output_tokens_details":{"thinking_tokens":700},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1172},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":7015,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXtAosTLoX3CqLR19MN8","requestRef":"14c5fc94-45b7-4384-9529-ba981f7667bb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4f599634-0e88-4d95-a3ee-3d1b893c1a31","timestamp":"2026-10-30T05:38:51.546Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"4f599634-0e88-4d95-a3ee-3d1b893c1a31","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtAp9a1BiTqR9kZHjhn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PeBvzjL1JNLnptCtxG1H5a","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"T2-simulation: coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nRegenerates out/results.json, out/table1.csv, out/figure1.png from data/design.csv only.\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data/design.csv\"\nOUT = \"out\"\ndesign = pd.read_csv(DATA)\ndata_issues = []\n\ndef key(est, G, icc):\n    return f\"{est}_G{int(G)}_icc{float(icc):.1f}\"\n\ndef simulate_cell(row):\n    G, icc, R, B = int(row.G), float(row.icc), int(row.R), int(row.B)\n    mu, level = float(row.mu), float(row.nominal_level)\n    n = np.array([int(s) for s in str(row.cluster_sizes).split(\"|\")])\n    N = int(n.sum())\n    assert len(n) == G, \"cluster_sizes length != G\"\n    assert N == int(row.N), \"sum(cluster_sizes) != N\"\n    var_exact = (icc * np.sum(n**2) + (1 - icc) * N) / N**2\n    rng = np.random.default_rng(int(row.seed))\n    # draw order (fixed, deterministic): U (R,G), E (R,N), bootstrap weights (R,B,G)\n    U = rng.normal(0.0, np.sqrt(icc), size=(R, G)) if icc > 0 else np.zeros((R, G))\n    E = rng.normal(0.0, np.sqrt(1 - icc), size=(R, N))\n    W = rng.choice(np.array([-1.0, 1.0]), size=(R, B, G))\n    cl = np.repeat(np.arange(G), n)\n    Y = mu + U[:, cl] + E                      # (R,N)\n    ybar = Y.mean(axis=1)                      # (R,)\n    ehat = Y - ybar[:, None]\n    S = np.zeros((R, G))\n    for g in range(G):\n        S[:, g] = ehat[:, cl == g].sum(axis=1)  # cluster residual sums\n    V0 = (S**2).sum(axis=1) / N**2\n    V1 = G / (G - 1) * V0\n    z = stats.norm.ppf(1 - (1 - level) / 2)    # 1.95996...; task says 1.96\n    z = 1.96\n    tcrit = stats.t.ppf(1 - (1 - level) / 2, G - 1)\n    hw0 = z * np.sqrt(V0)\n    hw1 = tcrit * np.sqrt(V1)\n    # Wild cluster bootstrap-t (unrestricted, Rademacher), using cluster sums:\n    # ybar* - ybar = D = sum_g w_g S_g / N ; bootstrap residual cluster sums = w_g S_g - n_g D\n    WS = W * S[:, None, :]                     # (R,B,G)\n    D = WS.sum(axis=2) / N                     # (R,B)\n    Sstar = WS - n[None, None, :] * D[:, :, None]\n    V1s = G / (G - 1) * (Sstar**2).sum(axis=2) / N**2\n    tstar = np.abs(D) / np.sqrt(V1s)\n    q = np.quantile(tstar, level, axis=1)      # numpy default (linear interpolation) 0.95 quantile\n    hwb = q * np.sqrt(V1)\n    out = {}\n    for est, hw, V in [(\"cr0\", hw0, V0), (\"cr1\", hw1, V1), (\"wcb\", hwb, None)]:\n        cov = np.mean(np.abs(ybar - mu) <= hw)\n        out[est] = dict(coverage=float(cov), mc_se=float(np.sqrt(cov * (1 - cov) / R)),\n                        rel_bias=None if V is None else float(V.mean() / var_exact),\n                        rel_bias_mcse=None if V is None else float(V.std(ddof=1) / np.sqrt(R) / var_exact),\n                        hw=float(hw.mean()))\n    extra = dict(var_exact=float(var_exact), emp_var_ybar=float(ybar.var(ddof=1)),\n                 n_distinct_abs_t_patterns=int(2 ** (G - 1)), mean_q=float(q.mean()), tcrit=float(tcrit))\n    return out, extra\n\nrows, cov, se, rb, rbse, extras = [], {}, {}, {}, {}, {}\nfor _, row in design.iterrows():\n    res, extra = simulate_cell(row)\n    extras[row.cell_id] = extra\n    for est in [\"cr0\", \"cr1\", \"wcb\"]:\n        r = res[est]; k = key(est, row.G, row.icc)\n        cov[k] = r[\"coverage\"]; se[k] = r[\"mc_se\"]\n        if r[\"rel_bias\"] is not None:\n            rb[k] = r[\"rel_bias\"]; rbse[k] = r[\"rel_bias_mcse\"]\n        rows.append(dict(estimator=est, cell_id=row.cell_id, G=int(row.G), icc=float(row.icc),\n                         coverage=r[\"coverage\"], mc_se_coverage=r[\"mc_se\"],\n                         rel_bias_var=r[\"rel_bias\"], mean_ci_halfwidth=r[\"hw\"], R=int(row.R)))\n\nif (design.G < 10).any():\n    data_issues.append({\"issue\": \"With G=5 and Rademacher weights there are only 2^5=32 distinct weight vectors \"\n                        \"(16 distinct |t*| values), so the B=399 bootstrap draws repeat these few values and the \"\n                        \"0.95 quantile of |t*| is coarse.\",\n                        \"handling\": \"Kept the specified design (B=399, Rademacher) unchanged; flagged as a limitation in the report.\"})\n\ntable = pd.DataFrame(rows)\ntable.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\nfailure = sorted([k for k, v in cov.items() if v < 0.90])\nnominal = sorted([k for k, v in cov.items() if abs(v - 0.95) <= 2 * se[k]])\ng5 = {est: float(np.mean([cov[key(est, 5, i)] for i in [0.0, 0.2, 0.5]])) for est in [\"cr0\", \"cr1\", \"wcb\"]}\nbest = max(g5, key=g5.get)\n\nresults = {\n    \"task_id\": \"T2-simulation\",\n    \"R\": int(design.R.iloc[0]), \"B\": int(design.B.iloc[0]), \"n_cells\": int(len(design)),\n    \"coverage\": cov, \"mc_se_coverage\": se, \"rel_bias_var\": rb,\n    \"failure_cells\": failure, \"n_failure_cells\": len(failure), \"nominal_cells\": nominal,\n    \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(design.R.sum()),\n    \"random_seed\": int(design.loc[design.cell_id == \"C1\", \"seed\"].iloc[0]),\n    # supplementary diagnostics\n    \"supplementary\": {\"rel_bias_var_mc_se\": rbse, \"mean_coverage_G5\": g5, \"cell_diagnostics\": extras},\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nGs = sorted(design.G.unique())\nfor ax, icc in zip(axes, [0.0, 0.2, 0.5]):\n    for est, m in [(\"cr0\", \"o\"), (\"cr1\", \"s\"), (\"wcb\", \"^\")]:\n        p = np.array([cov[key(est, G, icc)] for G in Gs]); s = np.array([se[key(est, G, icc)] for G in Gs])\n        ax.errorbar(Gs, p, yerr=1.96 * s, marker=m, capsize=3, label=est.upper())\n    ax.axhline(0.95, color=\"k\", ls=\"--\", lw=1, label=\"nominal 0.95\")\n    ax.axhline(0.90, color=\"r\", ls=\":\", lw=1, label=\"failure line 0.90\")\n    ax.set_xscale(\"log\"); ax.set_xticks(Gs); ax.set_xticklabels(Gs)\n    ax.set_title(f\"ICC = {icc}\"); ax.set_xlabel(\"number of clusters G\")\naxes[0].set_ylabel(\"coverage of nominal 95% CI (±1.96 MC SE)\")\naxes[0].legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(json.dumps({k: results[k] for k in [\"coverage\", \"rel_bias_var\", \"failure_cells\", \"nominal_cells\", \"best_procedure_small_G\"]}, indent=1))\nprint(json.dumps(extras, indent=1)); print(g5)\nEOF\ntime ~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1172,"cache_read_input_tokens":5067,"output_tokens":4003,"output_tokens_details":{"thinking_tokens":700},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1172},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtAosTLoX3CqLR19MN8","requestRef":"14c5fc94-45b7-4384-9529-ba981f7667bb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"58bb58c3-8e5b-49c1-ac32-0e164842cff7","timestamp":"2026-10-30T05:39:15.218Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"58bb58c3-8e5b-49c1-ac32-0e164842cff7","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PeBvzjL1JNLnptCtxG1H5a","type":"tool_result","content":"{\n \"coverage\": {\n  \"cr0_G5_icc0.0\": 0.7995,\n  \"cr1_G5_icc0.0\": 0.9195,\n  \"wcb_G5_icc0.0\": 0.8685,\n  \"cr0_G5_icc0.2\": 0.7195,\n  \"cr1_G5_icc0.2\": 0.8815,\n  \"wcb_G5_icc0.2\": 0.845,\n  \"cr0_G5_icc0.5\": 0.7205,\n  \"cr1_G5_icc0.5\": 0.8795,\n  \"wcb_G5_icc0.5\": 0.855,\n  \"cr0_G10_icc0.0\": 0.877,\n  \"cr1_G10_icc0.0\": 0.934,\n  \"wcb_G10_icc0.0\": 0.9275,\n  \"cr0_G10_icc0.2\": 0.859,\n  \"cr1_G10_icc0.2\": 0.9165,\n  \"wcb_G10_icc0.2\": 0.936,\n  \"cr0_G10_icc0.5\": 0.853,\n  \"cr1_G10_icc0.5\": 0.917,\n  \"wcb_G10_icc0.5\": 0.9345,\n  \"cr0_G30_icc0.0\": 0.935,\n  \"cr1_G30_icc0.0\": 0.951,\n  \"wcb_G30_icc0.0\": 0.9545,\n  \"cr0_G30_icc0.2\": 0.924,\n  \"cr1_G30_icc0.2\": 0.94,\n  \"wcb_G30_icc0.2\": 0.948,\n  \"cr0_G30_icc0.5\": 0.916,\n  \"cr1_G30_icc0.5\": 0.929,\n  \"wcb_G30_icc0.5\": 0.94\n },\n \"rel_bias_var\": {\n  \"cr0_G5_icc0.0\": 0.6543958179633829,\n  \"cr1_G5_icc0.0\": 0.8179947724542287,\n  \"cr0_G5_icc0.2\": 0.5073384003531096,\n  \"cr1_G5_icc0.2\": 0.6341730004413871,\n  \"cr0_G5_icc0.5\": 0.5054037231287982,\n  \"cr1_G5_icc0.5\": 0.6317546539109978,\n  \"cr0_G10_icc0.0\": 0.8532993449980574,\n  \"cr1_G10_icc0.0\": 0.9481103833311749,\n  \"cr0_G10_icc0.2\": 0.7595252021609881,\n  \"cr1_G10_icc0.2\": 0.8439168912899868,\n  \"cr0_G10_icc0.5\": 0.7668304455993856,\n  \"cr1_G10_icc0.5\": 0.8520338284437619,\n  \"cr0_G30_icc0.0\": 0.9400491633774785,\n  \"cr1_G30_icc0.0\": 0.9724646517698056,\n  \"cr0_G30_icc0.2\": 0.9185077380114064,\n  \"cr1_G30_icc0.2\": 0.9501804186324896,\n  \"cr0_G30_icc0.5\": 0.9153772013849211,\n  \"cr1_G30_icc0.5\": 0.9469419324671597\n },\n \"failure_cells\": [\n  \"cr0_G10_icc0.0\",\n  \"cr0_G10_icc0.2\",\n  \"cr0_G10_icc0.5\",\n  \"cr0_G5_icc0.0\",\n  \"cr0_G5_icc0.2\",\n  \"cr0_G5_icc0.5\",\n  \"cr1_G5_icc0.2\",\n  \"cr1_G5_icc0.5\",\n  \"wcb_G5_icc0.0\",\n  \"wcb_G5_icc0.2\",\n  \"wcb_G5_icc0.5\"\n ],\n \"nominal_cells\": [\n  \"cr1_G30_icc0.0\",\n  \"cr1_G30_icc0.2\",\n  \"wcb_G30_icc0.0\",\n  \"wcb_G30_icc0.2\",\n  \"wcb_G30_icc0.5\"\n ],\n \"best_procedure_small_G\": \"cr1\"\n}\n{\n \"C1\": {\n  \"var_exact\": 0.0078125,\n  \"emp_var_ybar\": 0.00757812061781907,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 3.054707824986178,\n  \"tcrit\": 2.7764451051977987\n },\n \"C2\": {\n  \"var_exact\": 0.0734375,\n  \"emp_var_ybar\": 0.07539240016119497,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 3.926638864865446,\n  \"tcrit\": 2.7764451051977987\n },\n \"C3\": {\n  \"var_exact\": 0.171875,\n  \"emp_var_ybar\": 0.1697750281577108,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 4.113240827670463,\n  \"tcrit\": 2.7764451051977987\n },\n \"C4\": {\n  \"var_exact\": 0.003787878787878788,\n  \"emp_var_ybar\": 0.003774468765421817,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.4643900108190526,\n  \"tcrit\": 2.262157162854099\n },\n \"C5\": {\n  \"var_exact\": 0.035169880624426074,\n  \"emp_var_ybar\": 0.034675290918919584,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.7857632856588017,\n  \"tcrit\": 2.262157162854099\n },\n \"C6\": {\n  \"var_exact\": 0.08224288337924701,\n  \"emp_var_ybar\": 0.08168608496708261,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.789884703379564,\n  \"tcrit\": 2.262157162854099\n },\n \"C7\": {\n  \"var_exact\": 0.0011574074074074073,\n  \"emp_var_ybar\": 0.0010795754031075135,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.097027898512898,\n  \"tcrit\": 2.0452296421327034\n },\n \"C8\": {\n  \"var_exact\": 0.011213991769547326,\n  \"emp_var_ybar\": 0.011011044129950015,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.165677700965893,\n  \"tcrit\": 2.0452296421327034\n },\n \"C9\": {\n  \"var_exact\": 0.0262988683127572,\n  \"emp_var_ybar\": 0.027478848671620475,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.175522981723053,\n  \"tcrit\": 2.0452296421327034\n }\n}\n{'cr0': 0.7465, 'cr1': 0.8935, 'wcb': 0.8561666666666667}\n~/wp29/venv/bin/python out/analysis.py  1,93s user 0,17s system 99% cpu 2,111 total","is_error":false}]},"uuid":"6d714d38-8ff2-4b05-af05-17c837b25d9a","timestamp":"2026-10-30T05:39:17.366Z","toolUseResult":{"stdout":"{\n \"coverage\": {\n  \"cr0_G5_icc0.0\": 0.7995,\n  \"cr1_G5_icc0.0\": 0.9195,\n  \"wcb_G5_icc0.0\": 0.8685,\n  \"cr0_G5_icc0.2\": 0.7195,\n  \"cr1_G5_icc0.2\": 0.8815,\n  \"wcb_G5_icc0.2\": 0.845,\n  \"cr0_G5_icc0.5\": 0.7205,\n  \"cr1_G5_icc0.5\": 0.8795,\n  \"wcb_G5_icc0.5\": 0.855,\n  \"cr0_G10_icc0.0\": 0.877,\n  \"cr1_G10_icc0.0\": 0.934,\n  \"wcb_G10_icc0.0\": 0.9275,\n  \"cr0_G10_icc0.2\": 0.859,\n  \"cr1_G10_icc0.2\": 0.9165,\n  \"wcb_G10_icc0.2\": 0.936,\n  \"cr0_G10_icc0.5\": 0.853,\n  \"cr1_G10_icc0.5\": 0.917,\n  \"wcb_G10_icc0.5\": 0.9345,\n  \"cr0_G30_icc0.0\": 0.935,\n  \"cr1_G30_icc0.0\": 0.951,\n  \"wcb_G30_icc0.0\": 0.9545,\n  \"cr0_G30_icc0.2\": 0.924,\n  \"cr1_G30_icc0.2\": 0.94,\n  \"wcb_G30_icc0.2\": 0.948,\n  \"cr0_G30_icc0.5\": 0.916,\n  \"cr1_G30_icc0.5\": 0.929,\n  \"wcb_G30_icc0.5\": 0.94\n },\n \"rel_bias_var\": {\n  \"cr0_G5_icc0.0\": 0.6543958179633829,\n  \"cr1_G5_icc0.0\": 0.8179947724542287,\n  \"cr0_G5_icc0.2\": 0.5073384003531096,\n  \"cr1_G5_icc0.2\": 0.6341730004413871,\n  \"cr0_G5_icc0.5\": 0.5054037231287982,\n  \"cr1_G5_icc0.5\": 0.6317546539109978,\n  \"cr0_G10_icc0.0\": 0.8532993449980574,\n  \"cr1_G10_icc0.0\": 0.9481103833311749,\n  \"cr0_G10_icc0.2\": 0.7595252021609881,\n  \"cr1_G10_icc0.2\": 0.8439168912899868,\n  \"cr0_G10_icc0.5\": 0.7668304455993856,\n  \"cr1_G10_icc0.5\": 0.8520338284437619,\n  \"cr0_G30_icc0.0\": 0.9400491633774785,\n  \"cr1_G30_icc0.0\": 0.9724646517698056,\n  \"cr0_G30_icc0.2\": 0.9185077380114064,\n  \"cr1_G30_icc0.2\": 0.9501804186324896,\n  \"cr0_G30_icc0.5\": 0.9153772013849211,\n  \"cr1_G30_icc0.5\": 0.9469419324671597\n },\n \"failure_cells\": [\n  \"cr0_G10_icc0.0\",\n  \"cr0_G10_icc0.2\",\n  \"cr0_G10_icc0.5\",\n  \"cr0_G5_icc0.0\",\n  \"cr0_G5_icc0.2\",\n  \"cr0_G5_icc0.5\",\n  \"cr1_G5_icc0.2\",\n  \"cr1_G5_icc0.5\",\n  \"wcb_G5_icc0.0\",\n  \"wcb_G5_icc0.2\",\n  \"wcb_G5_icc0.5\"\n ],\n \"nominal_cells\": [\n  \"cr1_G30_icc0.0\",\n  \"cr1_G30_icc0.2\",\n  \"wcb_G30_icc0.0\",\n  \"wcb_G30_icc0.2\",\n  \"wcb_G30_icc0.5\"\n ],\n \"best_procedure_small_G\": \"cr1\"\n}\n{\n \"C1\": {\n  \"var_exact\": 0.0078125,\n  \"emp_var_ybar\": 0.00757812061781907,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 3.054707824986178,\n  \"tcrit\": 2.7764451051977987\n },\n \"C2\": {\n  \"var_exact\": 0.0734375,\n  \"emp_var_ybar\": 0.07539240016119497,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 3.926638864865446,\n  \"tcrit\": 2.7764451051977987\n },\n \"C3\": {\n  \"var_exact\": 0.171875,\n  \"emp_var_ybar\": 0.1697750281577108,\n  \"n_distinct_abs_t_patterns\": 16,\n  \"mean_q\": 4.113240827670463,\n  \"tcrit\": 2.7764451051977987\n },\n \"C4\": {\n  \"var_exact\": 0.003787878787878788,\n  \"emp_var_ybar\": 0.003774468765421817,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.4643900108190526,\n  \"tcrit\": 2.262157162854099\n },\n \"C5\": {\n  \"var_exact\": 0.035169880624426074,\n  \"emp_var_ybar\": 0.034675290918919584,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.7857632856588017,\n  \"tcrit\": 2.262157162854099\n },\n \"C6\": {\n  \"var_exact\": 0.08224288337924701,\n  \"emp_var_ybar\": 0.08168608496708261,\n  \"n_distinct_abs_t_patterns\": 512,\n  \"mean_q\": 2.789884703379564,\n  \"tcrit\": 2.262157162854099\n },\n \"C7\": {\n  \"var_exact\": 0.0011574074074074073,\n  \"emp_var_ybar\": 0.0010795754031075135,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.097027898512898,\n  \"tcrit\": 2.0452296421327034\n },\n \"C8\": {\n  \"var_exact\": 0.011213991769547326,\n  \"emp_var_ybar\": 0.011011044129950015,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.165677700965893,\n  \"tcrit\": 2.0452296421327034\n },\n \"C9\": {\n  \"var_exact\": 0.0262988683127572,\n  \"emp_var_ybar\": 0.027478848671620475,\n  \"n_distinct_abs_t_patterns\": 536870912,\n  \"mean_q\": 2.175522981723053,\n  \"tcrit\": 2.0452296421327034\n }\n}\n{'cr0': 0.7465, 'cr1': 0.8935, 'wcb': 0.8561666666666667}\n~/wp29/venv/bin/python out/analysis.py  1,93s user 0,17s system 99% cpu 2,111 total","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"58bb58c3-8e5b-49c1-ac32-0e164842cff7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"c3106a6b009ce007c1970a5b99a7e8c5bbdbd13c3261f9d666c6b1672461a4a5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PeBvzjL1JNLnptCtxG1H5a","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"T2-simulation: coverage of CR0, CR1 and wild-cluster-bootstrap-t intervals for a mean.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nRegenerates out/results.json, out/table1.csv, out/figure1.png from data/design.csv only.\n\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nDATA = \"data/design.csv\"\nOUT = \"out\"\ndesign = pd.read_csv(DATA)\ndata_issues = []\n\ndef key(est, G, icc):\n    return f\"{est}_G{int(G)}_icc{float(icc):.1f}\"\n\ndef simulate_cell(row):\n    G, icc, R, B = int(row.G), float(row.icc), int(row.R), int(row.B)\n    mu, level = float(row.mu), float(row.nominal_level)\n    n = np.array([int(s) for s in str(row.cluster_sizes).split(\"|\")])\n    N = int(n.sum())\n    assert len(n) == G, \"cluster_sizes length != G\"\n    assert N == int(row.N), \"sum(cluster_sizes) != N\"\n    var_exact = (icc * np.sum(n**2) + (1 - icc) * N) / N**2\n    rng = np.random.default_rng(int(row.seed))\n    # draw order (fixed, deterministic): U (R,G), E (R,N), bootstrap weights (R,B,G)\n    U = rng.normal(0.0, np.sqrt(icc), size=(R, G)) if icc > 0 else np.zeros((R, G))\n    E = rng.normal(0.0, np.sqrt(1 - icc), size=(R, N))\n    W = rng.choice(np.array([-1.0, 1.0]), size=(R, B, G))\n    cl = np.repeat(np.arange(G), n)\n    Y = mu + U[:, cl] + E                      # (R,N)\n    ybar = Y.mean(axis=1)                      # (R,)\n    ehat = Y - ybar[:, None]\n    S = np.zeros((R, G))\n    for g in range(G):\n        S[:, g] = ehat[:, cl == g].sum(axis=1)  # cluster residual sums\n    V0 = (S**2).sum(axis=1) / N**2\n    V1 = G / (G - 1) * V0\n    z = stats.norm.ppf(1 - (1 - level) / 2)    # 1.95996...; task says 1.96\n    z = 1.96\n    tcrit = stats.t.ppf(1 - (1 - level) / 2, G - 1)\n    hw0 = z * np.sqrt(V0)\n    hw1 = tcrit * np.sqrt(V1)\n    # Wild cluster bootstrap-t (unrestricted, Rademacher), using cluster sums:\n    # ybar* - ybar = D = sum_g w_g S_g / N ; bootstrap residual cluster sums = w_g S_g - n_g D\n    WS = W * S[:, None, :]                     # (R,B,G)\n    D = WS.sum(axis=2) / N                     # (R,B)\n    Sstar = WS - n[None, None, :] * D[:, :, None]\n    V1s = G / (G - 1) * (Sstar**2).sum(axis=2) / N**2\n    tstar = np.abs(D) / np.sqrt(V1s)\n    q = np.quantile(tstar, level, axis=1)      # numpy default (linear interpolation) 0.95 quantile\n    hwb = q * np.sqrt(V1)\n    out = {}\n    for est, hw, V in [(\"cr0\", hw0, V0), (\"cr1\", hw1, V1), (\"wcb\", hwb, None)]:\n        cov = np.mean(np.abs(ybar - mu) <= hw)\n        out[est] = dict(coverage=float(cov), mc_se=float(np.sqrt(cov * (1 - cov) / R)),\n                        rel_bias=None if V is None else float(V.mean() / var_exact),\n                        rel_bias_mcse=None if V is None else float(V.std(ddof=1) / np.sqrt(R) / var_exact),\n                        hw=float(hw.mean()))\n    extra = dict(var_exact=float(var_exact), emp_var_ybar=float(ybar.var(ddof=1)),\n                 n_distinct_abs_t_patterns=int(2 ** (G - 1)), mean_q=float(q.mean()), tcrit=float(tcrit))\n    return out, extra\n\nrows, cov, se, rb, rbse, extras = [], {}, {}, {}, {}, {}\nfor _, row in design.iterrows():\n    res, extra = simulate_cell(row)\n    extras[row.cell_id] = extra\n    for est in [\"cr0\", \"cr1\", \"wcb\"]:\n        r = res[est]; k = key(est, row.G, row.icc)\n        cov[k] = r[\"coverage\"]; se[k] = r[\"mc_se\"]\n        if r[\"rel_bias\"] is not None:\n            rb[k] = r[\"rel_bias\"]; rbse[k] = r[\"rel_bias_mcse\"]\n        rows.append(dict(estimator=est, cell_id=row.cell_id, G=int(row.G), icc=float(row.icc),\n                         coverage=r[\"coverage\"], mc_se_coverage=r[\"mc_se\"],\n                         rel_bias_var=r[\"rel_bias\"], mean_ci_halfwidth=r[\"hw\"], R=int(row.R)))\n\nif (design.G < 10).any():\n    data_issues.append({\"issue\": \"With G=5 and Rademacher weights there are only 2^5=32 distinct weight vectors \"\n                        \"(16 distinct |t*| values), so the B=399 bootstrap draws repeat these few values and the \"\n                        \"0.95 quantile of |t*| is coarse.\",\n                        \"handling\": \"Kept the specified design (B=399, Rademacher) unchanged; flagged as a limitation in the report.\"})\n\ntable = pd.DataFrame(rows)\ntable.to_csv(os.path.join(OUT, \"table1.csv\"), index=False)\n\nfailure = sorted([k for k, v in cov.items() if v < 0.90])\nnominal = sorted([k for k, v in cov.items() if abs(v - 0.95) <= 2 * se[k]])\ng5 = {est: float(np.mean([cov[key(est, 5, i)] for i in [0.0, 0.2, 0.5]])) for est in [\"cr0\", \"cr1\", \"wcb\"]}\nbest = max(g5, key=g5.get)\n\nresults = {\n    \"task_id\": \"T2-simulation\",\n    \"R\": int(design.R.iloc[0]), \"B\": int(design.B.iloc[0]), \"n_cells\": int(len(design)),\n    \"coverage\": cov, \"mc_se_coverage\": se, \"rel_bias_var\": rb,\n    \"failure_cells\": failure, \"n_failure_cells\": len(failure), \"nominal_cells\": nominal,\n    \"best_procedure_small_G\": best,\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": {\"design.csv\": int(len(design))},\n    \"n_analysed\": int(design.R.sum()),\n    \"random_seed\": int(design.loc[design.cell_id == \"C1\", \"seed\"].iloc[0]),\n    # supplementary diagnostics\n    \"supplementary\": {\"rel_bias_var_mc_se\": rbse, \"mean_coverage_G5\": g5, \"cell_diagnostics\": extras},\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\nfig, axes = plt.subplots(1, 3, figsize=(13, 4.2), sharey=True)\nGs = sorted(design.G.unique())\nfor ax, icc in zip(axes, [0.0, 0.2, 0.5]):\n    for est, m in [(\"cr0\", \"o\"), (\"cr1\", \"s\"), (\"wcb\", \"^\")]:\n        p = np.array([cov[key(est, G, icc)] for G in Gs]); s = np.array([se[key(est, G, icc)] for G in Gs])\n        ax.errorbar(Gs, p, yerr=1.96 * s, marker=m, capsize=3, label=est.upper())\n    ax.axhline(0.95, color=\"k\", ls=\"--\", lw=1, label=\"nominal 0.95\")\n    ax.axhline(0.90, color=\"r\", ls=\":\", lw=1, label=\"failure line 0.90\")\n    ax.set_xscale(\"log\"); ax.set_xticks(Gs); ax.set_xticklabels(Gs)\n    ax.set_title(f\"ICC = {icc}\"); ax.set_xlabel(\"number of clusters G\")\naxes[0].set_ylabel(\"coverage of nominal 95% CI (±1.96 MC SE)\")\naxes[0].legend(fontsize=8, loc=\"lower right\")\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=130)\nprint(json.dumps({k: results[k] for k in [\"coverage\", \"rel_bias_var\", \"failure_cells\", \"nominal_cells\", \"best_procedure_small_G\"]}, indent=1))\nprint(json.dumps(extras, indent=1)); print(g5)\nEOF\ntime ~/wp29/venv/bin/python out/analysis.py"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"e1a1bc8b-e743-44ff-bf71-79f94fd0830c","timestamp":"2026-10-30T05:39:17.369Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"14c5fc94-45b7-4384-9529-ba981f7667bb","keep":6,"tail":["c3106a6b009ce007c1970a5b99a7e8c5bbdbd13c3261f9d666c6b1672461a4a5","ee71e24c38d5f04a4d763b76a14171e947b43c35f9dc3d6fb01e338a45086083"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"cbfff481c0388344cc99ea9fb8cb8724e20b32e1e88aa33e8d236dab36a71900"}
{"parentUuid":"6d714d38-8ff2-4b05-af05-17c837b25d9a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtDKLYe8yqxEKeVLzU5","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6416,"cache_read_input_tokens":6239,"output_tokens":1177,"output_tokens_details":{"thinking_tokens":483},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":6416},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":4828,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXtDK4gL5xkUwhh49x5j","requestRef":"e1a1bc8b-e743-44ff-bf71-79f94fd0830c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8614311a-399b-4776-9126-924f7b32ba0f","timestamp":"2026-10-30T05:39:23.471Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"8614311a-399b-4776-9126-924f7b32ba0f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtDKLYe8yqxEKeVLzU5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PKrR8LWYZNTPPzQrkaJXMu","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace(\"\"\"    z = stats.norm.ppf(1 - (1 - level) / 2)    # 1.95996...; task says 1.96\n    z = 1.96\n\"\"\",\"\"\"    z = 1.96                                   # as specified in TASK.md\n\"\"\")\ns=s.replace(\"\"\"    extra = dict(var_exact\"\"\",\"\"\"    # analytic E[V_CR0]/Var(ybar): T_g = cluster sum of (y-mu), a_g = Var(T_g), S_g = T_g - (n_g/N) sum T\n    a = icc * n**2 + (1 - icc) * n\n    ES2 = a - 2 * (n / N) * a + (n / N) ** 2 * a.sum()\n    rb0_exact = float(ES2.sum() / N**2 / var_exact)\n    extra = dict(rel_bias_cr0_analytic=rb0_exact, rel_bias_cr1_analytic=rb0_exact * G / (G - 1), var_exact\"\"\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));o=json.load(open('/tmp/r1.json'))\nprint(r['coverage']==o['coverage'])\ns=r['supplementary']\nfor c,d in s['cell_diagnostics'].items(): print(c, round(d['rel_bias_cr0_analytic'],4), round(d['rel_bias_cr1_analytic'],4))\nfor k,v in s['rel_bias_var_mc_se'].items(): print(k, round(r['rel_bias_var'][k],4), round(v,4))\n\"; cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6416,"cache_read_input_tokens":6239,"output_tokens":1177,"output_tokens_details":{"thinking_tokens":483},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":6416},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtDK4gL5xkUwhh49x5j","requestRef":"e1a1bc8b-e743-44ff-bf71-79f94fd0830c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"165261c2-435a-4a69-904e-7848a11ccba3","timestamp":"2026-10-30T05:39:28.515Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"165261c2-435a-4a69-904e-7848a11ccba3","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"type":"atis-latch","atis":"","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"parentUuid":"165261c2-435a-4a69-904e-7848a11ccba3","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PKrR8LWYZNTPPzQrkaJXMu","type":"tool_result","content":"True\nC1 0.6641 0.8301\nC2 0.4995 0.6244\nC3 0.4883 0.6104\nC4 0.8393 0.9326\nC5 0.7599 0.8443\nC6 0.7544 0.8382\nC7 0.9486 0.9813\nC8 0.9267 0.9587\nC9 0.9253 0.9572\ncr0_G5_icc0.0 0.6544 0.0124\ncr1_G5_icc0.0 0.818 0.0155\ncr0_G5_icc0.2 0.5073 0.0126\ncr1_G5_icc0.2 0.6342 0.0157\ncr0_G5_icc0.5 0.5054 0.0126\ncr1_G5_icc0.5 0.6318 0.0157\ncr0_G10_icc0.0 0.8533 0.0116\ncr1_G10_icc0.0 0.9481 0.0129\ncr0_G10_icc0.2 0.7595 0.0132\ncr1_G10_icc0.2 0.8439 0.0147\ncr0_G10_icc0.5 0.7668 0.0144\ncr1_G10_icc0.5 0.852 0.016\ncr0_G30_icc0.0 0.94 0.0066\ncr1_G30_icc0.0 0.9725 0.0068\ncr0_G30_icc0.2 0.9185 0.0085\ncr1_G30_icc0.2 0.9502 0.0088\ncr0_G30_icc0.5 0.9154 0.0083\ncr1_G30_icc0.5 0.9469 0.0085\nestimator,cell_id,G,icc,coverage,mc_se_coverage,rel_bias_var,mean_ci_halfwidth,R\ncr0,C1,5,0.0,0.7995,0.008952646256833787,0.6543958179633829,0.12945791363306097,2000\ncr1,C1,5,0.0,0.9195,0.006083574196144895,0.8179947724542287,0.20502963091931684,2000\nwcb,C1,5,0.0,0.8685,0.007556710593902613,,0.24573964493135197,2000\ncr0,C2,5,0.2,0.7195,0.01004539073406306,0.5073384003531096,0.3363597922614171,2000\ncr1,C2,5,0.2,0.8815,0.007226954752867907,0.6341730004413871,0.5327115363447698,2000\nwcb,C2,5,0.2,0.845,0.008092434738692676,,0.8763211552225286,2000\ncr0,C3,5,0.5,0.7205,0.010034434463386564,0.5054037231287982,0.5098423713216832,2000\ncr1,C3,5,0.5,0.8795,0.007279414468211026,0.6317546539109978,0.8074654556491971,2000\nwcb,C3,5,0.5,0.855,0.007873214083206426,,1.4049160819326203,2000\ncr0,C4,10,0.0,0.877,0.007344079247938437,0.8532993449980574,0.10674308709433686,2000\ncr1,C4,10,0.0,0.934,0.005551756478809205,0.9481103833311749,0.12986293285620532,2000\nwcb,C4,10,0.0,0.9275,0.005798437289477226,,0.1447336834390087,2000\ncr0,C5,10,0.2,0.859,0.007781998457979801,0.7595252021609881,0.30019002401528055,2000\ncr1,C5,10,0.2,0.9165,0.006185780063985464,0.8439168912899868,0.3652091952179194,2000\nwcb,C5,10,0.2,0.936,0.00547284204047586,,0.4627505497323718,2000\ncr0,C6,10,0.5,0.853,0.007918049002121673,0.7668304455993856,0.45866713816216975,2000\ncr1,C6,10,0.5,0.917,0.006168914004912047,0.8520338284437619,0.5580114027792793,2000\nwcb,C6,10,0.5,0.9345,0.005532167296819575,,0.7086118074460948,2000\ncr0,C7,30,0.0,0.935,0.005512485827646179,0.9400491633774785,0.06386677305359995,2000\ncr1,C7,30,0.0,0.951,0.004826955562256609,0.9724646517698056,0.06778328447345469,2000\nwcb,C7,30,0.0,0.9545,0.004659922209651143,,0.06965215759362171,2000\ncr0,C8,30,0.2,0.924,0.005925537950262405,0.9185077380114064,0.19481781032685644,2000\ncr1,C8,30,0.2,0.94,0.005310367218940704,0.9501804186324896,0.20676465126550664,2000\nwcb,C8,30,0.2,0.948,0.0049646752159632785,,0.21942998914538384,2000\ncr0,C9,30,0.5,0.916,0.006202580108309766,0.9153772013849211,0.2981667448851098,2000\ncr1,C9,30,0.5,0.929,0.005742778073371805,0.9469419324671597,0.31645126758024267,2000\nwcb,C9,30,0.5,0.94,0.005310367218940704,,0.337236866394146,2000","is_error":false}]},"uuid":"9ac3bc91-1543-4f10-9d52-fa127e224220","timestamp":"2026-10-30T05:39:30.604Z","toolUseResult":{"stdout":"True\nC1 0.6641 0.8301\nC2 0.4995 0.6244\nC3 0.4883 0.6104\nC4 0.8393 0.9326\nC5 0.7599 0.8443\nC6 0.7544 0.8382\nC7 0.9486 0.9813\nC8 0.9267 0.9587\nC9 0.9253 0.9572\ncr0_G5_icc0.0 0.6544 0.0124\ncr1_G5_icc0.0 0.818 0.0155\ncr0_G5_icc0.2 0.5073 0.0126\ncr1_G5_icc0.2 0.6342 0.0157\ncr0_G5_icc0.5 0.5054 0.0126\ncr1_G5_icc0.5 0.6318 0.0157\ncr0_G10_icc0.0 0.8533 0.0116\ncr1_G10_icc0.0 0.9481 0.0129\ncr0_G10_icc0.2 0.7595 0.0132\ncr1_G10_icc0.2 0.8439 0.0147\ncr0_G10_icc0.5 0.7668 0.0144\ncr1_G10_icc0.5 0.852 0.016\ncr0_G30_icc0.0 0.94 0.0066\ncr1_G30_icc0.0 0.9725 0.0068\ncr0_G30_icc0.2 0.9185 0.0085\ncr1_G30_icc0.2 0.9502 0.0088\ncr0_G30_icc0.5 0.9154 0.0083\ncr1_G30_icc0.5 0.9469 0.0085\nestimator,cell_id,G,icc,coverage,mc_se_coverage,rel_bias_var,mean_ci_halfwidth,R\ncr0,C1,5,0.0,0.7995,0.008952646256833787,0.6543958179633829,0.12945791363306097,2000\ncr1,C1,5,0.0,0.9195,0.006083574196144895,0.8179947724542287,0.20502963091931684,2000\nwcb,C1,5,0.0,0.8685,0.007556710593902613,,0.24573964493135197,2000\ncr0,C2,5,0.2,0.7195,0.01004539073406306,0.5073384003531096,0.3363597922614171,2000\ncr1,C2,5,0.2,0.8815,0.007226954752867907,0.6341730004413871,0.5327115363447698,2000\nwcb,C2,5,0.2,0.845,0.008092434738692676,,0.8763211552225286,2000\ncr0,C3,5,0.5,0.7205,0.010034434463386564,0.5054037231287982,0.5098423713216832,2000\ncr1,C3,5,0.5,0.8795,0.007279414468211026,0.6317546539109978,0.8074654556491971,2000\nwcb,C3,5,0.5,0.855,0.007873214083206426,,1.4049160819326203,2000\ncr0,C4,10,0.0,0.877,0.007344079247938437,0.8532993449980574,0.10674308709433686,2000\ncr1,C4,10,0.0,0.934,0.005551756478809205,0.9481103833311749,0.12986293285620532,2000\nwcb,C4,10,0.0,0.9275,0.005798437289477226,,0.1447336834390087,2000\ncr0,C5,10,0.2,0.859,0.007781998457979801,0.7595252021609881,0.30019002401528055,2000\ncr1,C5,10,0.2,0.9165,0.006185780063985464,0.8439168912899868,0.3652091952179194,2000\nwcb,C5,10,0.2,0.936,0.00547284204047586,,0.4627505497323718,2000\ncr0,C6,10,0.5,0.853,0.007918049002121673,0.7668304455993856,0.45866713816216975,2000\ncr1,C6,10,0.5,0.917,0.006168914004912047,0.8520338284437619,0.5580114027792793,2000\nwcb,C6,10,0.5,0.9345,0.005532167296819575,,0.7086118074460948,2000\ncr0,C7,30,0.0,0.935,0.005512485827646179,0.9400491633774785,0.06386677305359995,2000\ncr1,C7,30,0.0,0.951,0.004826955562256609,0.9724646517698056,0.06778328447345469,2000\nwcb,C7,30,0.0,0.9545,0.004659922209651143,,0.06965215759362171,2000\ncr0,C8,30,0.2,0.924,0.005925537950262405,0.9185077380114064,0.19481781032685644,2000\ncr1,C8,30,0.2,0.94,0.005310367218940704,0.9501804186324896,0.20676465126550664,2000\nwcb,C8,30,0.2,0.948,0.0049646752159632785,,0.21942998914538384,2000\ncr0,C9,30,0.5,0.916,0.006202580108309766,0.9153772013849211,0.2981667448851098,2000\ncr1,C9,30,0.5,0.929,0.005742778073371805,0.9469419324671597,0.31645126758024267,2000\nwcb,C9,30,0.5,0.94,0.005310367218940704,,0.337236866394146,2000","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"165261c2-435a-4a69-904e-7848a11ccba3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"a61ab720700e9119f1425642824b72e87ebd58cdd4cdfb41453e9c19fed1c33e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PKrR8LWYZNTPPzQrkaJXMu","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace(\"\"\"    z = stats.norm.ppf(1 - (1 - level) / 2)    # 1.95996...; task says 1.96\n    z = 1.96\n\"\"\",\"\"\"    z = 1.96                                   # as specified in TASK.md\n\"\"\")\ns=s.replace(\"\"\"    extra = dict(var_exact\"\"\",\"\"\"    # analytic E[V_CR0]/Var(ybar): T_g = cluster sum of (y-mu), a_g = Var(T_g), S_g = T_g - (n_g/N) sum T\n    a = icc * n**2 + (1 - icc) * n\n    ES2 = a - 2 * (n / N) * a + (n / N) ** 2 * a.sum()\n    rb0_exact = float(ES2.sum() / N**2 / var_exact)\n    extra = dict(rel_bias_cr0_analytic=rb0_exact, rel_bias_cr1_analytic=rb0_exact * G / (G - 1), var_exact\"\"\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && cp out/results.json /tmp/r1.json && ~/wp29/venv/bin/python out/analysis.py >/dev/null && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));o=json.load(open('/tmp/r1.json'))\nprint(r['coverage']==o['coverage'])\ns=r['supplementary']\nfor c,d in s['cell_diagnostics'].items(): print(c, round(d['rel_bias_cr0_analytic'],4), round(d['rel_bias_cr1_analytic'],4))\nfor k,v in s['rel_bias_var_mc_se'].items(): print(k, round(r['rel_bias_var'][k],4), round(v,4))\n\"; cat out/table1.csv"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"5cda4bb7-0322-461a-a2f8-61302ba81325","timestamp":"2026-10-30T05:39:30.607Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"e1a1bc8b-e743-44ff-bf71-79f94fd0830c","keep":8,"tail":["a61ab720700e9119f1425642824b72e87ebd58cdd4cdfb41453e9c19fed1c33e","67c6bd384d20fd60f9a2476ad7edf2d260a0b37597f833bfccb7233091b6a3f1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e68f07f5dd4d1ad5b47b21a4edee9b801d58994ffbbad048c4775228c226812c"}
{"parentUuid":"9ac3bc91-1543-4f10-9d52-fa127e224220","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtEHvFDEWy42wHa1Ljz","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSxAgKEAgTGAI4AUIIdGhpbmtpbmcSDGEzM2mkQPs1OYk+PhoMG7BoaiGyezTu6+xtIjB9K/YKkmpPFxx66teA1vLtoURJMwTeXhovTr8eIbNYfqI8cs9ERpD4AmVcnKdfV7Mq4QdENkkIlcZA1ryfp+V711H18WhRCgQcDWMEX6klJCaXmYR4KGYU2GC2IxL4kmCf7LZrqTm6aIYAE4EClSUJHtYrn37x/wqYQQVRb/Dzsii1R52il1ZAiFkSkksD+qnIJ/d/Pvugk+VCmLN7YfQBWr0BG3aAc9II7S+35v0STrhQBwBENyB+xlu9BBLFNNwXq2x8ocyEKRedzHgERHT9VFttYzYKVhyyndXAuSZg4yCnwiPEXq46j3F79/x0ELYYUTkUw2aByFLkfkU9GL8+NJxgEmHyW1K+cAjgoPk/vQPSxEhADSejL8T3NamTuIscVF5FIl3dQxQbEgKJI6z+WqGgvLJx3ksFWah0nwRQBvfHZZsHaAA2G83sUpG1yyJ9veY9vMzxhUqcNreWl8d9mchh4i0mWowJ9KDF+TnlWpza62lmPsm8RDWOHq0Qfvc+fXKjkOUXWxXsFGkXGTDQQhkWcuHnFJozqUCPx5OREssW+xgnWGQrkC+FLVaUUu7upydJZx+Oy4EgNNo0gj2RUHXBbr/Q75sP7n/oiaX4cEcPoTo0707icBDkeFxtsam8BqB49zTU+DI9Ce/Wttfut/eMHO81cpj2WwpcI+zZlYnAo12PIbdcM9AVMRZNbmEZJGpYXSxV6UnfNqFOUmzO3kPQDJKvkcoZqOGlyImAARhnja2qHpu1QWl7rRh5tSmZ+OacXc2i5OZxHdsN9d+508TbET1fk8DxIgrZ2vdGb/AxtzSAR4py+mbuKafJPHZf67EC2Shm7oP1x+a0FBDJCqDfO2U0lsQ2LnZ9zPuhmItKK3ajd4tHwWkVvX+9VQ7YY067ydxjqKcoDyZ6eIFVWh1BZu2X68/Tlcq457rsjgLIapRsHiP9ojdEsGfwD8FdcRBxc6Wd1yne4W/dzwyenaoNlQRiW5j27z4MdARE+XUd0rK0jHgqBwkY1fb4UaOUlrZP2JpIi/16YxpDL4JJ1m4CrevAvv61eFL/7mhKxjYkXJpJkVl8jY8QIne9/2kIKIq1QqHZGfbRiXU+YuxSVzRfca+cnsyNi3b2nKx+liSjN6UiFK5CiW3m6Tgeib+cwm1l0SNd8GOcJXQ3qBWEsMHOtGRr7Q+RaojyrRZpVNY1BCcgUpAzGgTtxEmq2YjmfBh9tdLLiuCogdM8xvpchUREIIp9xliZQ0ivb3JRLuXN5qnA2VzQD1hncXFQtLjVKJuRBp/j9O4gIA1LGR0fUuzBStRvBT9qPxok+pKxdT7EJwms8y4k2Y1mpl/9OSa19bL7wWBcBVrq4Twq4h5l1GVJ6z+MN5OlbS8i0hQ03wsqdoIYAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2967,"cache_read_input_tokens":12655,"output_tokens":4657,"output_tokens_details":{"thinking_tokens":163},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2967},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1388,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXtEHestbCuMVTcF9rss","requestRef":"5cda4bb7-0322-461a-a2f8-61302ba81325","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2abb428d-0d4b-4db2-b4e6-cfdf5a081b3a","timestamp":"2026-10-30T05:39:33.005Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"2abb428d-0d4b-4db2-b4e6-cfdf5a081b3a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtEHvFDEWy42wHa1Ljz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01YJy4rdSGGQMrSefWSBq2gY","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Finite-sample coverage of cluster-robust confidence intervals for a mean (T2-simulation)\n\nAll numbers below come from `out/analysis.py` (run: `~/wp29/venv/bin/python out/analysis.py` from the workspace root; ~2 s; deterministic given the seeds in `data/design.csv`). Full numbers: `out/table1.csv`, `out/results.json`; figure: `out/figure1.png`.\n\n## Question\nWith few clusters, how close to 95% is the actual coverage of three cluster-robust intervals for a population mean μ — CR0 with a normal critical value, CR1 with a t(G−1) critical value, and the wild cluster bootstrap-t (WCB; Rademacher, unrestricted) — how biased are the CR0/CR1 variance estimators relative to the exact Var(ȳ), and in which (procedure, cell) pairs does coverage fall below 0.90?\n\n## Data and exclusions\nThere are no observed data. The only input is `data/design.csv` (9 rows = 9 cells: G ∈ {5,10,30} × ICC ∈ {0,0.2,0.5}). Cluster sizes repeat 8,16,32,64 (so cluster sizes are unbalanced, ranging from 8 to 64). The script checks that each `cluster_sizes` list has G entries and sums to `N`; both checks pass. Nothing is excluded. R = 2000 replications and B = 399 bootstrap draws per replication in each cell, so 18,000 replications in total.\n\n## Method\n- DGP: y_ig = μ + u_g + e_ig, u_g ~ N(0, ICC), e_ig ~ N(0, 1−ICC), μ = 0. Each cell uses its own `numpy.random.default_rng(seed)`. Draws are made in a fixed order: U (R×G), then E (R×N), then the Rademacher weights (R×B×G).\n- CR0: V̂ = Σ_g(Σ_i ê_ig)²/N², interval ȳ ± 1.96√V̂. CR1: V̂·G/(G−1), interval ȳ ± t_{0.975,G−1}√V̂_CR1.\n- WCB: y* = ȳ + w_g ê_ig. The bootstrap is computed exactly through cluster residual sums S_g. The bootstrap estimate shifts by D = Σ w_g S_g / N, and the bootstrap cluster residual sums are w_g S_g − n_g D. From these, t* = D/√V̂*_CR1 and q = the 0.95 quantile of |t*| over B = 399 draws. The quantile uses `numpy.quantile`'s default linear-interpolation rule; this is a documented choice, since the task does not fix a quantile definition. The interval is ȳ ± q√V̂_CR1.\n- Coverage is the share of the R intervals that contain μ, with MC SE = √(p̂(1−p̂)/R). Relative bias is mean(V̂)/Var(ȳ), using the exact Var(ȳ) from TASK.md. A pair is a failure if p̂ < 0.90. It is \"consistent with nominal\" if |p̂ − 0.95| ≤ 2·MC SE.\n- Implementation checks: the empirical variance of ȳ across replications matches the exact Var(ȳ) in every cell (e.g. C1: 0.00758 vs 0.00781). I also derived E[V̂_CR0] in closed form. With T_g = Σ_i(y_ig−μ), a_g = Var(T_g) = ICC·n_g² + (1−ICC)·n_g, and S_g = T_g − (n_g/N)ΣT, the expectation is E[S_g²] = a_g(1 − 2n_g/N) + (n_g/N)²Σa. The simulated relative biases agree with this formula to within about 1.2 MC SE (e.g. C1 CR0: simulated 0.654 vs analytic 0.664; C3: 0.505 vs 0.488; C4: 0.853 vs 0.839). The analytic values are stored in `results.json → supplementary.cell_diagnostics`.\n\n## Results\nCoverage of the nominal 95% intervals (MC SE ≈ 0.005–0.010):\n\n| G | ICC | CR0 | CR1 | WCB | relbias CR0 | relbias CR1 |\n|---|---|---|---|---|---|---|\n| 5 | 0.0 | 0.800 | 0.920 | 0.869 | 0.654 | 0.818 |\n| 5 | 0.2 | 0.720 | 0.882 | 0.845 | 0.507 | 0.634 |\n| 5 | 0.5 | 0.721 | 0.880 | 0.855 | 0.505 | 0.632 |\n| 10 | 0.0 | 0.877 | 0.934 | 0.928 | 0.853 | 0.948 |\n| 10 | 0.2 | 0.859 | 0.917 | 0.936 | 0.760 | 0.844 |\n| 10 | 0.5 | 0.853 | 0.917 | 0.935 | 0.767 | 0.852 |\n| 30 | 0.0 | 0.935 | 0.951 | 0.955 | 0.940 | 0.972 |\n| 30 | 0.2 | 0.924 | 0.940 | 0.948 | 0.919 | 0.950 |\n| 30 | 0.5 | 0.916 | 0.929 | 0.940 | 0.915 | 0.947 |\n\n- **Failure regimes (coverage < 0.90): 11 of 27 pairs.** They are CR0 at G = 5 and G = 10 (all ICCs), CR1 at G = 5 with ICC 0.2 and 0.5, and WCB at G = 5 (all ICCs). No pair fails at G = 30.\n- **Consistent with nominal (|p̂−0.95| ≤ 2 MC SE): 5 pairs.** These are CR1 at G=30 with ICC 0 and 0.2, and WCB at G=30 for all three ICCs. Every other pair, including CR0 everywhere, undercovers by a statistically clear margin.\n- **Variance bias:** CR0 underestimates Var(ȳ) everywhere. It recovers only about 50% of the variance at G=5 with ICC>0, about 76% at G=10, and about 92% at G=30. Multiplying by G/(G−1) (CR1) helps but does not remove the bias at small G (0.63 at G=5, ICC>0). The downward bias is larger with ICC>0 than with ICC=0. That is because, under clustering, the variance is dominated by the largest clusters (size 64), whose own residual sums are shrunk the most when the overall mean is subtracted.\n- **Best procedure at G=5:** CR1, with mean coverage 0.894 over the three G=5 cells, compared with WCB 0.856 and CR0 0.747. At G=10 with ICC>0, WCB is closest to nominal (0.936/0.935 vs CR1 0.917). At G=30, WCB and CR1 are both close to nominal, and CR0 still undercovers by 1.5–3.4 points.\n- The WCB intervals at G=5 are on average *wider* than the CR1 intervals (e.g. mean half-width 0.88 vs 0.53 at ICC 0.2) yet cover less often. Its bootstrap critical value varies strongly from replication to replication (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78). It tends to be small precisely in samples where V̂ is also small, so it does not repair the undercoverage.\n\n## Uncertainty\n- Coverage: each coverage estimate is a binomial proportion over R = 2000 independent replications, with MC SE between 0.0047 (near 0.95) and 0.0100 (near 0.72). A 95% MC interval is therefore about ±0.01–0.02. Classifications near a threshold are fragile. The closest calls are CR1 G=5 ICC=0 (0.920, 3.3 SE above 0.90), CR1 G=5 ICC=0.5 (0.880, 2.8 SE below), and CR1 G=30 ICC=0.5 (0.929, about 3.7 SE below 0.95, so not nominal). WCB/CR1 differences within a cell are computed on the same simulated samples, so they are positively correlated; the marginal SEs therefore overstate the uncertainty of those differences.\n- Relative bias: the MC SE of mean(V̂)/Var(ȳ) is about 0.012–0.016 at G=5/10 and about 0.007–0.009 at G=30 (stored in `supplementary.rel_bias_var_mc_se`). The closed-form check agrees within that error.\n- Bootstrap error: with B = 399, q itself is noisy. At G=5, Rademacher weights take only 2^5 = 32 distinct values, i.e. 16 distinct |t*| values, so q is computed from heavily repeated values. This is a property of the specified design, not an estimation error, and it is recorded in `data_issues`.\n- Seeds: one seed per cell, as in the design. Results for other seeds would differ by roughly the MC SE.\n\n## Limitations and open issues\n- **Distributions:** the design uses Gaussian cluster effects and errors only. Skewed or heavy-tailed u_g/e_ig, which typically worsen small-G performance, are not covered.\n- **Heterogeneity:** ICC and error variance are the same in every cluster. One fixed size pattern (8–64) is used, so size–effect correlation and heteroskedasticity across clusters are absent. Other degrees of size imbalance are not explored.\n- **Regressors:** only an intercept-only mean is studied. Regression slopes, treated-cluster designs (e.g. few treated clusters in DiD), and fixed effects are not covered. Leverage-adjusted CR2/CR3 (Bell–McCaffrey) variants are not included.\n- **Other weights/variants:** only unrestricted Rademacher WCB-t with a symmetric |t| quantile is studied. The restricted (null-imposed) WCB, Webb six-point or Mammen weights (relevant at G=5 because Rademacher gives only 32 distinct draws), percentile-t equal-tailed intervals, and alternative quantile definitions are not studied.\n- The grid is coarse (G ∈ {5,10,30}, three ICCs), so the G at which each procedure becomes reliable is located only roughly.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"CR0 with a normal critical value undercovers in every cell and fails (coverage < 0.90) at G = 5 and G = 10 for all ICCs.\",\"results_keys\":[\"coverage\",\"failure_cells\"],\"status\":\"supported\",\"caveat\":\"Gaussian DGP, one cluster-size pattern.\"},\n {\"id\":\"C2\",\"text\":\"11 of 27 (procedure, cell) pairs are failure regimes; all are at G = 5 or G = 10, none at G = 30.\",\"results_keys\":[\"failure_cells\",\"n_failure_cells\"],\"status\":\"supported\",\"caveat\":\"CR1 G=5 classifications are within ~3 MC SE of the 0.90 threshold.\"},\n {\"id\":\"C3\",\"text\":\"CR0 and CR1 variance estimators are biased downward; at G = 5 with ICC > 0, CR0 recovers about 50% and CR1 about 63% of the exact Var(ybar).\",\"results_keys\":[\"rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"MC SE of relative bias about 0.013-0.016; confirmed by a closed-form expectation.\"},\n {\"id\":\"C4\",\"text\":\"CR1 has the highest average coverage over the G = 5 cells (0.894), ahead of WCB (0.856) and CR0 (0.747).\",\"results_keys\":[\"best_procedure_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"WCB at G=5 uses Rademacher weights with only 32 distinct draws; other weight schemes may perform differently.\"},\n {\"id\":\"C5\",\"text\":\"Only 5 pairs are consistent with nominal 95% coverage: CR1 at G=30 (ICC 0, 0.2) and WCB at G=30 (all ICCs).\",\"results_keys\":[\"nominal_cells\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"The criterion is a 2-MC-SE band; failing to reject nominal coverage is not proof of exact coverage.\"},\n {\"id\":\"C6\",\"text\":\"At G = 10 with ICC > 0, WCB is closer to nominal than CR1 (about 0.935 vs 0.917).\",\"results_keys\":[\"coverage\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"The difference is about 2-3 marginal MC SEs; the paired design makes it more precise than the marginal SEs suggest, but no formal paired test is reported.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls -la out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2967,"cache_read_input_tokens":12655,"output_tokens":4657,"output_tokens_details":{"thinking_tokens":163},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2967},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtEHestbCuMVTcF9rss","requestRef":"5cda4bb7-0322-461a-a2f8-61302ba81325","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"277bb0d1-3ed4-460a-a0e7-c8e17546acc8","timestamp":"2026-10-30T05:40:10.036Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"277bb0d1-3ed4-460a-a0e7-c8e17546acc8","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YJy4rdSGGQMrSefWSBq2gY","type":"tool_result","content":"total 224\ndrwxr-xr-x@ 8 simon-pierreboucher  staff    256 oct.  30 01:40 .\ndrwxr-xr-x  7 simon-pierreboucher  staff    224 oct.  30 01:39 ..\n-rw-r--r--@ 1 simon-pierreboucher  staff   6718 oct.  30 01:39 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher  staff   1817 oct.  30 01:40 claims.json\n-rw-r--r--@ 1 simon-pierreboucher  staff  79891 oct.  30 01:39 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher  staff   7602 oct.  30 01:40 report.md\n-rw-r--r--@ 1 simon-pierreboucher  staff   7781 oct.  30 01:39 results.json\n-rw-r--r--@ 1 simon-pierreboucher  staff   2199 oct.  30 01:39 table1.csv","is_error":false}]},"uuid":"dc2184d0-6122-4c42-990e-85506e55dd5b","timestamp":"2026-10-30T05:40:10.113Z","toolUseResult":{"stdout":"total 224\ndrwxr-xr-x@ 8 simon-pierreboucher  staff    256 oct.  30 01:40 .\ndrwxr-xr-x  7 simon-pierreboucher  staff    224 oct.  30 01:39 ..\n-rw-r--r--@ 1 simon-pierreboucher  staff   6718 oct.  30 01:39 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher  staff   1817 oct.  30 01:40 claims.json\n-rw-r--r--@ 1 simon-pierreboucher  staff  79891 oct.  30 01:39 figure1.png\n-rw-r--r--@ 1 simon-pierreboucher  staff   7602 oct.  30 01:40 report.md\n-rw-r--r--@ 1 simon-pierreboucher  staff   7781 oct.  30 01:39 results.json\n-rw-r--r--@ 1 simon-pierreboucher  staff   2199 oct.  30 01:39 table1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"277bb0d1-3ed4-460a-a0e7-c8e17546acc8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"3a7a8f7ac3513ea9f23beb313196f9eaac2490578279d32db9c7b9e2313064e2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01YJy4rdSGGQMrSefWSBq2gY","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Finite-sample coverage of cluster-robust confidence intervals for a mean (T2-simulation)\n\nAll numbers below come from `out/analysis.py` (run: `~/wp29/venv/bin/python out/analysis.py` from the workspace root; ~2 s; deterministic given the seeds in `data/design.csv`). Full numbers: `out/table1.csv`, `out/results.json`; figure: `out/figure1.png`.\n\n## Question\nWith few clusters, how close to 95% is the actual coverage of three cluster-robust intervals for a population mean μ — CR0 with a normal critical value, CR1 with a t(G−1) critical value, and the wild cluster bootstrap-t (WCB; Rademacher, unrestricted) — how biased are the CR0/CR1 variance estimators relative to the exact Var(ȳ), and in which (procedure, cell) pairs does coverage fall below 0.90?\n\n## Data and exclusions\nThere are no observed data. The only input is `data/design.csv` (9 rows = 9 cells: G ∈ {5,10,30} × ICC ∈ {0,0.2,0.5}). Cluster sizes repeat 8,16,32,64 (so cluster sizes are unbalanced, ranging from 8 to 64). The script checks that each `cluster_sizes` list has G entries and sums to `N`; both checks pass. Nothing is excluded. R = 2000 replications and B = 399 bootstrap draws per replication in each cell, so 18,000 replications in total.\n\n## Method\n- DGP: y_ig = μ + u_g + e_ig, u_g ~ N(0, ICC), e_ig ~ N(0, 1−ICC), μ = 0. Each cell uses its own `numpy.random.default_rng(seed)`. Draws are made in a fixed order: U (R×G), then E (R×N), then the Rademacher weights (R×B×G).\n- CR0: V̂ = Σ_g(Σ_i ê_ig)²/N², interval ȳ ± 1.96√V̂. CR1: V̂·G/(G−1), interval ȳ ± t_{0.975,G−1}√V̂_CR1.\n- WCB: y* = ȳ + w_g ê_ig. The bootstrap is computed exactly through cluster residual sums S_g. The bootstrap estimate shifts by D = Σ w_g S_g / N, and the bootstrap cluster residual sums are w_g S_g − n_g D. From these, t* = D/√V̂*_CR1 and q = the 0.95 quantile of |t*| over B = 399 draws. The quantile uses `numpy.quantile`'s default linear-interpolation rule; this is a documented choice, since the task does not fix a quantile definition. The interval is ȳ ± q√V̂_CR1.\n- Coverage is the share of the R intervals that contain μ, with MC SE = √(p̂(1−p̂)/R). Relative bias is mean(V̂)/Var(ȳ), using the exact Var(ȳ) from TASK.md. A pair is a failure if p̂ < 0.90. It is \"consistent with nominal\" if |p̂ − 0.95| ≤ 2·MC SE.\n- Implementation checks: the empirical variance of ȳ across replications matches the exact Var(ȳ) in every cell (e.g. C1: 0.00758 vs 0.00781). I also derived E[V̂_CR0] in closed form. With T_g = Σ_i(y_ig−μ), a_g = Var(T_g) = ICC·n_g² + (1−ICC)·n_g, and S_g = T_g − (n_g/N)ΣT, the expectation is E[S_g²] = a_g(1 − 2n_g/N) + (n_g/N)²Σa. The simulated relative biases agree with this formula to within about 1.2 MC SE (e.g. C1 CR0: simulated 0.654 vs analytic 0.664; C3: 0.505 vs 0.488; C4: 0.853 vs 0.839). The analytic values are stored in `results.json → supplementary.cell_diagnostics`.\n\n## Results\nCoverage of the nominal 95% intervals (MC SE ≈ 0.005–0.010):\n\n| G | ICC | CR0 | CR1 | WCB | relbias CR0 | relbias CR1 |\n|---|---|---|---|---|---|---|\n| 5 | 0.0 | 0.800 | 0.920 | 0.869 | 0.654 | 0.818 |\n| 5 | 0.2 | 0.720 | 0.882 | 0.845 | 0.507 | 0.634 |\n| 5 | 0.5 | 0.721 | 0.880 | 0.855 | 0.505 | 0.632 |\n| 10 | 0.0 | 0.877 | 0.934 | 0.928 | 0.853 | 0.948 |\n| 10 | 0.2 | 0.859 | 0.917 | 0.936 | 0.760 | 0.844 |\n| 10 | 0.5 | 0.853 | 0.917 | 0.935 | 0.767 | 0.852 |\n| 30 | 0.0 | 0.935 | 0.951 | 0.955 | 0.940 | 0.972 |\n| 30 | 0.2 | 0.924 | 0.940 | 0.948 | 0.919 | 0.950 |\n| 30 | 0.5 | 0.916 | 0.929 | 0.940 | 0.915 | 0.947 |\n\n- **Failure regimes (coverage < 0.90): 11 of 27 pairs.** They are CR0 at G = 5 and G = 10 (all ICCs), CR1 at G = 5 with ICC 0.2 and 0.5, and WCB at G = 5 (all ICCs). No pair fails at G = 30.\n- **Consistent with nominal (|p̂−0.95| ≤ 2 MC SE): 5 pairs.** These are CR1 at G=30 with ICC 0 and 0.2, and WCB at G=30 for all three ICCs. Every other pair, including CR0 everywhere, undercovers by a statistically clear margin.\n- **Variance bias:** CR0 underestimates Var(ȳ) everywhere. It recovers only about 50% of the variance at G=5 with ICC>0, about 76% at G=10, and about 92% at G=30. Multiplying by G/(G−1) (CR1) helps but does not remove the bias at small G (0.63 at G=5, ICC>0). The downward bias is larger with ICC>0 than with ICC=0. That is because, under clustering, the variance is dominated by the largest clusters (size 64), whose own residual sums are shrunk the most when the overall mean is subtracted.\n- **Best procedure at G=5:** CR1, with mean coverage 0.894 over the three G=5 cells, compared with WCB 0.856 and CR0 0.747. At G=10 with ICC>0, WCB is closest to nominal (0.936/0.935 vs CR1 0.917). At G=30, WCB and CR1 are both close to nominal, and CR0 still undercovers by 1.5–3.4 points.\n- The WCB intervals at G=5 are on average *wider* than the CR1 intervals (e.g. mean half-width 0.88 vs 0.53 at ICC 0.2) yet cover less often. Its bootstrap critical value varies strongly from replication to replication (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78). It tends to be small precisely in samples where V̂ is also small, so it does not repair the undercoverage.\n\n## Uncertainty\n- Coverage: each coverage estimate is a binomial proportion over R = 2000 independent replications, with MC SE between 0.0047 (near 0.95) and 0.0100 (near 0.72). A 95% MC interval is therefore about ±0.01–0.02. Classifications near a threshold are fragile. The closest calls are CR1 G=5 ICC=0 (0.920, 3.3 SE above 0.90), CR1 G=5 ICC=0.5 (0.880, 2.8 SE below), and CR1 G=30 ICC=0.5 (0.929, about 3.7 SE below 0.95, so not nominal). WCB/CR1 differences within a cell are computed on the same simulated samples, so they are positively correlated; the marginal SEs therefore overstate the uncertainty of those differences.\n- Relative bias: the MC SE of mean(V̂)/Var(ȳ) is about 0.012–0.016 at G=5/10 and about 0.007–0.009 at G=30 (stored in `supplementary.rel_bias_var_mc_se`). The closed-form check agrees within that error.\n- Bootstrap error: with B = 399, q itself is noisy. At G=5, Rademacher weights take only 2^5 = 32 distinct values, i.e. 16 distinct |t*| values, so q is computed from heavily repeated values. This is a property of the specified design, not an estimation error, and it is recorded in `data_issues`.\n- Seeds: one seed per cell, as in the design. Results for other seeds would differ by roughly the MC SE.\n\n## Limitations and open issues\n- **Distributions:** the design uses Gaussian cluster effects and errors only. Skewed or heavy-tailed u_g/e_ig, which typically worsen small-G performance, are not covered.\n- **Heterogeneity:** ICC and error variance are the same in every cluster. One fixed size pattern (8–64) is used, so size–effect correlation and heteroskedasticity across clusters are absent. Other degrees of size imbalance are not explored.\n- **Regressors:** only an intercept-only mean is studied. Regression slopes, treated-cluster designs (e.g. few treated clusters in DiD), and fixed effects are not covered. Leverage-adjusted CR2/CR3 (Bell–McCaffrey) variants are not included.\n- **Other weights/variants:** only unrestricted Rademacher WCB-t with a symmetric |t| quantile is studied. The restricted (null-imposed) WCB, Webb six-point or Mammen weights (relevant at G=5 because Rademacher gives only 32 distinct draws), percentile-t equal-tailed intervals, and alternative quantile definitions are not studied.\n- The grid is coarse (G ∈ {5,10,30}, three ICCs), so the G at which each procedure becomes reliable is located only roughly.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"CR0 with a normal critical value undercovers in every cell and fails (coverage < 0.90) at G = 5 and G = 10 for all ICCs.\",\"results_keys\":[\"coverage\",\"failure_cells\"],\"status\":\"supported\",\"caveat\":\"Gaussian DGP, one cluster-size pattern.\"},\n {\"id\":\"C2\",\"text\":\"11 of 27 (procedure, cell) pairs are failure regimes; all are at G = 5 or G = 10, none at G = 30.\",\"results_keys\":[\"failure_cells\",\"n_failure_cells\"],\"status\":\"supported\",\"caveat\":\"CR1 G=5 classifications are within ~3 MC SE of the 0.90 threshold.\"},\n {\"id\":\"C3\",\"text\":\"CR0 and CR1 variance estimators are biased downward; at G = 5 with ICC > 0, CR0 recovers about 50% and CR1 about 63% of the exact Var(ybar).\",\"results_keys\":[\"rel_bias_var\"],\"status\":\"supported\",\"caveat\":\"MC SE of relative bias about 0.013-0.016; confirmed by a closed-form expectation.\"},\n {\"id\":\"C4\",\"text\":\"CR1 has the highest average coverage over the G = 5 cells (0.894), ahead of WCB (0.856) and CR0 (0.747).\",\"results_keys\":[\"best_procedure_small_G\",\"coverage\"],\"status\":\"supported\",\"caveat\":\"WCB at G=5 uses Rademacher weights with only 32 distinct draws; other weight schemes may perform differently.\"},\n {\"id\":\"C5\",\"text\":\"Only 5 pairs are consistent with nominal 95% coverage: CR1 at G=30 (ICC 0, 0.2) and WCB at G=30 (all ICCs).\",\"results_keys\":[\"nominal_cells\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"The criterion is a 2-MC-SE band; failing to reject nominal coverage is not proof of exact coverage.\"},\n {\"id\":\"C6\",\"text\":\"At G = 10 with ICC > 0, WCB is closer to nominal than CR1 (about 0.935 vs 0.917).\",\"results_keys\":[\"coverage\",\"mc_se_coverage\"],\"status\":\"supported\",\"caveat\":\"The difference is about 2-3 marginal MC SEs; the paired design makes it more precise than the marginal SEs suggest, but no formal paired test is reported.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; ls -la out"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"0a461e6b-0811-4780-bc1d-25e566797c18","timestamp":"2026-10-30T05:40:10.117Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"5cda4bb7-0322-461a-a2f8-61302ba81325","keep":10,"tail":["3a7a8f7ac3513ea9f23beb313196f9eaac2490578279d32db9c7b9e2313064e2","4d083f147d117cb413722501ed7b2a32148af9a3f172d51f4435696c63400167"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9c475309ac35f0ff6f4a1bd55c37172b8620445721362b00fc81a84b93edd81a"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"dc2184d0-6122-4c42-990e-85506e55dd5b","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"type":"atis-latch","atis":"","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"parentUuid":"dc2184d0-6122-4c42-990e-85506e55dd5b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtHCryHu4qxiBoewP8T","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5011,"cache_read_input_tokens":15622,"output_tokens":499,"output_tokens_details":{"thinking_tokens":168},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5011},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1516,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXtHCaNASQVVbkkr6b9N","requestRef":"0a461e6b-0811-4780-bc1d-25e566797c18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"76386d28-c069-405a-b12e-5b338f2f7b33","timestamp":"2026-10-30T05:40:12.762Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"76386d28-c069-405a-b12e-5b338f2f7b33","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtHCryHu4qxiBoewP8T","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WGa2Kmkwv5GPqvMhV4W13D","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));s=r['supplementary']\nids={5:{0.0:'C1',0.2:'C2',0.5:'C3'},10:{0.0:'C4',0.2:'C5',0.5:'C6'},30:{0.0:'C7',0.2:'C8',0.5:'C9'}}\nm=0\nfor k,v in r['rel_bias_var'].items():\n  e,G,i=k.split('_');G=int(G[1:]);i=float(i[3:]);a=s['cell_diagnostics'][ids[G][i]]['rel_bias_%s_analytic'%e]\n  z=(v-a)/s['rel_bias_var_mc_se'][k];m=max(m,abs(z));print(k,round(z,2))\nprint(m)\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5011,"cache_read_input_tokens":15622,"output_tokens":499,"output_tokens_details":{"thinking_tokens":168},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":5011},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXtHCaNASQVVbkkr6b9N","requestRef":"0a461e6b-0811-4780-bc1d-25e566797c18","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"56991f0a-118c-4480-99b0-1992003eaca9","timestamp":"2026-10-30T05:40:14.897Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"56991f0a-118c-4480-99b0-1992003eaca9","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WGa2Kmkwv5GPqvMhV4W13D","type":"tool_result","content":"cr0_G5_icc0.0 -0.78\ncr1_G5_icc0.0 -0.78\ncr0_G5_icc0.2 0.62\ncr1_G5_icc0.2 0.62\ncr0_G5_icc0.5 1.36\ncr1_G5_icc0.5 1.36\ncr0_G10_icc0.0 1.2\ncr1_G10_icc0.0 1.2\ncr0_G10_icc0.2 -0.03\ncr1_G10_icc0.2 -0.03\ncr0_G10_icc0.5 0.87\ncr1_G10_icc0.5 0.87\ncr0_G30_icc0.0 -1.29\ncr1_G30_icc0.0 -1.29\ncr0_G30_icc0.2 -0.97\ncr1_G30_icc0.2 -0.97\ncr0_G30_icc0.5 -1.2\ncr1_G30_icc0.5 -1.2\n1.359157971967924","is_error":false}]},"uuid":"5645b376-f917-4a59-b5ff-51395d67c585","timestamp":"2026-10-30T05:40:14.940Z","toolUseResult":{"stdout":"cr0_G5_icc0.0 -0.78\ncr1_G5_icc0.0 -0.78\ncr0_G5_icc0.2 0.62\ncr1_G5_icc0.2 0.62\ncr0_G5_icc0.5 1.36\ncr1_G5_icc0.5 1.36\ncr0_G10_icc0.0 1.2\ncr1_G10_icc0.0 1.2\ncr0_G10_icc0.2 -0.03\ncr1_G10_icc0.2 -0.03\ncr0_G10_icc0.5 0.87\ncr1_G10_icc0.5 0.87\ncr0_G30_icc0.0 -1.29\ncr1_G30_icc0.0 -1.29\ncr0_G30_icc0.2 -0.97\ncr1_G30_icc0.2 -0.97\ncr0_G30_icc0.5 -1.2\ncr1_G30_icc0.5 -1.2\n1.359157971967924","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"56991f0a-118c-4480-99b0-1992003eaca9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"32e98f1d87f5d1aa2865db3ef76d9190a502f6186af4354a321c3b4987cf5db2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS2ggKEAgTGAI4AUIIdGhpbmtpbmcSDIqqVIObqefySuevzhoMOPe+j59G0iORSX4XIjC2Vyt9A9pmhpERqC16fkzvD+O3p64b3gWPJOvZ4MxfsElvOg63KQpdzvDoFxxZSmsq9wfjIXkullxfEfOjAsbM2mnDdBJ2n5d6l5MZVeobm98ssGeGxjGGMwiBtn1zy+0Chv6JwobT39qCdLJDUOXHWqJNLVdVBV44Tu0ah4G0zEwFOnkabEYGWEZhXfQY13nDzvzh2Bi4T61Hiu+DVzypPSBKaGmmxQytLSugc15aUvudbaH8UaL+nD2U1qI6Hy4cfDVvzzOJ7w9wIacHB9+BhnqP50Jv6I9n9hvsUaPCUmfrh/vgQ39cmxxlSiSethZTA6O3A65hpk8Ai79GQgXZlNObyQQxBoIAZ3JaTyiraLQum6LVrsQaXmFKeRd/d2/UJIGf/5uisa/ZNOzojp+Bsuqn88/xOk/4WIA7mZOEr1dqWFMRmft4iKJkC7SAoZ6aCnQJ4XHIUmjGlXWI+Ssox+hqOnM7Gn2ggWFQrO9M2QA8HjuPzywtE4ff1T0Vgs1bk1v4OWmqN/se9Xtk5qyYk/PLD+3iMT6wEujGC0V2IO9TOPlD/lqglF4b6jCEBheqeYatWzf7F+RAl/jR7Qq+/ndRTSuV9Rv4g1Twi7ydAZUyT7TKpe6KifTnw/l90RhJtpCGDdYRb4FAfmqJw20rrkABzpzfGNyZ0TFFuPqS5f1zaRkUyFsYiKx1Ei8G8TdZg1chBp1LTORskwSZMd/dxHeGtnANAgrXac4M0ogwHCDFctGZco7gi0IGbWmcq4hseZNcqnDIjpgni8bBiLToM5o3h9n4lgiGtaQZM9m0kStLYovZiYNzC1C6z2YtzoN4kD5tyidqsM+VZeQyD1SMwdnOrqcolpG2wk1TSNadXIcEJhMypqL+Mfkfr/q0PvNnfoKpq3/yWYj3f7cuO585tuV82x9qNf+3gf06vxMssK7LtIwhP4C6dsWpmXYm3bcyPrusIU8Ul74BIspOGeCWlH27b8d53+ERi5Me/ZPHAJk5/1X6vhUFCJda90aUx0mLmkEzA6zxKrFx5TfYwpljgiuP1X4gDQoyjolJ4lbV25x8kUZu9ORj26xxSw400TT9jcNS/stm7aQ5kAtE5RdceoYS/T/3X+0zO8CtZR8EsdO1xUExIYfh0WUNDOwJbT1DcFZ5ec7r6deOWXtiOULm3NeboMFZ5e3HUze0DqHNkLee22wXpQ2/rcN3IggcvWXMG+AFncoNMuK5brcHwCWyOFGMfFrVjEnXS7Ncd3asubGFBWBzB7ukjiYOBuj7YIDUX/wdicRfLPx9P2yXAAbWwKYj8kgQCAs3A7Be4iQkb6l0hOqZ8GiJWmEbyyp0uGN4uRcg05awN6PeFOjsaUifV1iBiHZ3AR3afJMg5mMRrD+yQxBl1v8/BxdxfM56xhaLcbaaTHAMShafGAE="},{"type":"tool_use","id":"toolu_01WGa2Kmkwv5GPqvMhV4W13D","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));s=r['supplementary']\nids={5:{0.0:'C1',0.2:'C2',0.5:'C3'},10:{0.0:'C4',0.2:'C5',0.5:'C6'},30:{0.0:'C7',0.2:'C8',0.5:'C9'}}\nm=0\nfor k,v in r['rel_bias_var'].items():\n  e,G,i=k.split('_');G=int(G[1:]);i=float(i[3:]);a=s['cell_diagnostics'][ids[G][i]]['rel_bias_%s_analytic'%e]\n  z=(v-a)/s['rel_bias_var_mc_se'][k];m=max(m,abs(z));print(k,round(z,2))\nprint(m)\""}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"4339800e-7872-491d-ac63-e0c2d8ab921d","timestamp":"2026-10-30T05:40:14.943Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"0a461e6b-0811-4780-bc1d-25e566797c18","keep":12,"tail":["32e98f1d87f5d1aa2865db3ef76d9190a502f6186af4354a321c3b4987cf5db2","99a67e95198143daef09764fe37e6a1c79ccd7b31d85af5a7391ad8fb55976b7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"649aae6c43763d968774f903813f540757269637ac67023b2222cd6b6189e985"}
{"parentUuid":"5645b376-f917-4a59-b5ff-51395d67c585","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtHZRjJpULQSxu8pGVP","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01P6tZb8qvzH2NcKMEkG8XtN","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('report.md').read()\ns=s.replace(\"to within about 1.2 MC SE\",\"to within 1.4 MC SE in every cell\")\ns=s.replace(\"The closed-form check agrees within that error.\",\"The closed-form check agrees within 1.4 MC SE in all 18 pairs.\")\ns=s.replace(\"Its bootstrap critical value varies strongly from replication to replication (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78). It tends to be small precisely in samples where V̂ is also small, so it does not repair the undercoverage.\",\n \"Its bootstrap critical value is larger on average (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78) but varies from replication to replication. The extra width therefore seems to go to samples that would have been covered anyway. I did not check this mechanism directly, e.g. via the joint distribution of q and V̂; it is a conjecture.\")\nopen('report.md','w').write(s)\nEOF\ngrep -n \"1.4 MC\\|conjecture\" report.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":827,"cache_read_input_tokens":20633,"output_tokens":433,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":827},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXtHZEKXoTcybsTs1jM4","requestRef":"4339800e-7872-491d-ac63-e0c2d8ab921d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"197ee2aa-d85a-42f6-b88f-80abab3fb95d","timestamp":"2026-10-30T05:40:18.666Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"197ee2aa-d85a-42f6-b88f-80abab3fb95d","isSidechain":false,"promptId":"17619da5-94f3-4749-8ff2-59445c105053","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P6tZb8qvzH2NcKMEkG8XtN","type":"tool_result","content":"16:- Implementation checks: the empirical variance of ȳ across replications matches the exact Var(ȳ) in every cell (e.g. C1: 0.00758 vs 0.00781). I also derived E[V̂_CR0] in closed form. With T_g = Σ_i(y_ig−μ), a_g = Var(T_g) = ICC·n_g² + (1−ICC)·n_g, and S_g = T_g − (n_g/N)ΣT, the expectation is E[S_g²] = a_g(1 − 2n_g/N) + (n_g/N)²Σa. The simulated relative biases agree with this formula to within 1.4 MC SE in every cell (e.g. C1 CR0: simulated 0.654 vs analytic 0.664; C3: 0.505 vs 0.488; C4: 0.853 vs 0.839). The analytic values are stored in `results.json → supplementary.cell_diagnostics`.\n37:- The WCB intervals at G=5 are on average *wider* than the CR1 intervals (e.g. mean half-width 0.88 vs 0.53 at ICC 0.2) yet cover less often. Its bootstrap critical value is larger on average (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78) but varies from replication to replication. The extra width therefore seems to go to samples that would have been covered anyway. I did not check this mechanism directly, e.g. via the joint distribution of q and V̂; it is a conjecture.\n41:- Relative bias: the MC SE of mean(V̂)/Var(ȳ) is about 0.012–0.016 at G=5/10 and about 0.007–0.009 at G=30 (stored in `supplementary.rel_bias_var_mc_se`). The closed-form check agrees within 1.4 MC SE in all 18 pairs.","is_error":false}]},"uuid":"d0779d7c-ee17-4835-a872-1eba3b5c0924","timestamp":"2026-10-30T05:40:18.715Z","toolUseResult":{"stdout":"16:- Implementation checks: the empirical variance of ȳ across replications matches the exact Var(ȳ) in every cell (e.g. C1: 0.00758 vs 0.00781). I also derived E[V̂_CR0] in closed form. With T_g = Σ_i(y_ig−μ), a_g = Var(T_g) = ICC·n_g² + (1−ICC)·n_g, and S_g = T_g − (n_g/N)ΣT, the expectation is E[S_g²] = a_g(1 − 2n_g/N) + (n_g/N)²Σa. The simulated relative biases agree with this formula to within 1.4 MC SE in every cell (e.g. C1 CR0: simulated 0.654 vs analytic 0.664; C3: 0.505 vs 0.488; C4: 0.853 vs 0.839). The analytic values are stored in `results.json → supplementary.cell_diagnostics`.\n37:- The WCB intervals at G=5 are on average *wider* than the CR1 intervals (e.g. mean half-width 0.88 vs 0.53 at ICC 0.2) yet cover less often. Its bootstrap critical value is larger on average (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78) but varies from replication to replication. The extra width therefore seems to go to samples that would have been covered anyway. I did not check this mechanism directly, e.g. via the joint distribution of q and V̂; it is a conjecture.\n41:- Relative bias: the MC SE of mean(V̂)/Var(ȳ) is about 0.012–0.016 at G=5/10 and about 0.007–0.009 at G=30 (stored in `supplementary.rel_bias_var_mc_se`). The closed-form check agrees within 1.4 MC SE in all 18 pairs.","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"197ee2aa-d85a-42f6-b88f-80abab3fb95d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018/out","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"api-request-blob","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","hash":"8bde7773fe3df91adbcfd94b67ef00b00f1e5b4cba9f5782692fac0aaf95c6d7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01P6tZb8qvzH2NcKMEkG8XtN","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('report.md').read()\ns=s.replace(\"to within about 1.2 MC SE\",\"to within 1.4 MC SE in every cell\")\ns=s.replace(\"The closed-form check agrees within that error.\",\"The closed-form check agrees within 1.4 MC SE in all 18 pairs.\")\ns=s.replace(\"Its bootstrap critical value varies strongly from replication to replication (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78). It tends to be small precisely in samples where V̂ is also small, so it does not repair the undercoverage.\",\n \"Its bootstrap critical value is larger on average (mean q ≈ 3.1–4.1 vs t_{0.975,4} = 2.78) but varies from replication to replication. The extra width therefore seems to go to samples that would have been covered anyway. I did not check this mechanism directly, e.g. via the joint distribution of q and V̂; it is a conjecture.\")\nopen('report.md','w').write(s)\nEOF\ngrep -n \"1.4 MC\\|conjecture\" report.md"}}]}}
{"type":"api-request","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","id":"9da43d43-5310-401a-8914-35dfd46129cc","timestamp":"2026-10-30T05:40:18.718Z","version":"2.1.314","querySource":"sdk","shapeHash":"f44365975a5768a9fa9fecc698c0b3a5c56bbebd0cc02491b42a19075fed9e6d","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"4339800e-7872-491d-ac63-e0c2d8ab921d","keep":14,"tail":["8bde7773fe3df91adbcfd94b67ef00b00f1e5b4cba9f5782692fac0aaf95c6d7","422bb40a70e5782d836916da9af44ecb7b5ba9b756f7395e69af441058cf7ac2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b278d1232eec8ef5505dfea7daff67188750ebcd48d79a551a1a7baddac5172b"}
{"parentUuid":"d0779d7c-ee17-4835-a872-1eba3b5c0924","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXtHqd4ofLzpZsWehH2U","type":"message","role":"assistant","content":[{"type":"text","text":"I ran the full simulation (9 cells × 2000 replications, 399 bootstrap draws each) and produced every required file. The study is complete. The script regenerates identical numbers on a second run and takes about 2 seconds.\n\n**Main results:**\n- **Failures (coverage below 0.90): 11 of 27 procedure–cell pairs.** CR0 fails at G = 5 and G = 10 for every ICC. CR1 fails at G = 5 when ICC is 0.2 or 0.5. The wild cluster bootstrap (WCB) fails at G = 5 for every ICC. Nothing fails at G = 30.\n- **Close to 95% (within 2 Monte Carlo standard errors): only 5 pairs.** These are CR1 at G = 30 with ICC 0 or 0.2, and WCB at G = 30 for all three ICCs. CR0 undercovers in every cell, by 1.5–3.4 points even at G = 30.\n- **Best procedure at G = 5: CR1**, with average coverage 0.894, against 0.856 for WCB and 0.747 for CR0. At G = 10 with ICC above 0, WCB comes closest to 95% (about 0.935, versus 0.917 for CR1).\n- **Variance estimates are too small.** At G = 5 with ICC above 0, CR0 captures only about 51% of the true variance of the sample mean and CR1 about 63%. At G = 30 the figures are about 92% and 95%.\n\n**Checks:** I derived the expected CR0 variance in closed form, and the simulated biases agree with it to within 1.4 Monte Carlo standard errors in all 18 pairs. The spread of the sample mean across replications also matches its exact variance in every cell.\n\n**Limitations:**\n- **Coarse bootstrap at G = 5:** with only 5 clusters the ±1 bootstrap weights allow just 32 distinct draws, so the bootstrap critical value is coarse. This comes from the specified design; I kept it and recorded it under `data_issues`.\n- **Unverified explanation:** WCB intervals at G = 5 are wider on average than CR1's yet cover less often. My explanation for this is a conjecture I did not check, and the report labels it as such.\n- **Quantile rule is my choice:** the task doesn't define how to take the 0.95 quantile of the bootstrap draws, so I used numpy's default interpolation.\n- **Narrow design:** it covers only normal data, equal variance across clusters, one cluster-size pattern, a mean with no regressors, and the unrestricted bootstrap with ±1 weights. The report lists what this leaves out.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `table1.csv`\n- `figure1.png`\n- `report.md`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1061,"cache_read_input_tokens":21460,"output_tokens":938,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1061},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXtHqMSV2MRjbeRZiBtn","requestRef":"9da43d43-5310-401a-8914-35dfd46129cc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8c03fe55-a4b4-489d-aca0-293067b37d9e","timestamp":"2026-10-30T05:40:27.049Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0018/out","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","version":"2.1.314","gitBranch":"HEAD"}
{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8c03fe55-a4b4-489d-aca0-293067b37d9e","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a"}
{"type":"cost-state","sessionId":"dbe7a8e4-4e7a-4dd3-b325-7dddec95190a","totalCostUSD":0.3588198,"totalAPIDuration":101497,"totalAPIDurationWithoutRetries":101492,"totalToolDuration":4445,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":106130,"startTime":1793338720927,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":11829,"thinkingTokens":1514,"cacheReadInputTokens":85414,"cacheCreationInputTokens":21017,"cacheCreationEphemeral5mInputTokens":21017,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3588198}},"hasUnknownModelCost":false}