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82 statistical analysis methods in R — regression, survival, Bayesian, meta-analysis, causal inference, SEM, IRT, clinical trial design, and more. JSON spec driven, reproducible, with mandatory effect sizes and assumption checks. Use when: user asks for statistical analysis, hypothesis testing, regression, ANOVA, t-test, chi-square, correlation, survival analysis, Cox regression, meta-analysis, propensity score, causal inference, SEM, IRT, power analysis, sample size calculation, time series forecasting, mixed models, Bayesian analysis, ROC/AUC, agreement/reliability, zero-inflated models, penalized regression, LASSO, group sequential design, or mentions R packages like ggplot2, brms, survival, metafor, lavaan, glmnet, mice, lme4, gee, dagitty, tmle. Multilingual triggers — EN: statistics, regression, significance, predict; ZH: 统计分析, 回归, 检验, 预测, 显著性, 生存分析, 元分析, 贝叶斯; JA: 統計分析, 回帰, 検定, 予測; KO: 통계분석, 회귀, 검정; ES: análisis estadístico, regresión; FR: analyse statistique, régression; DE: st…

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whyzsm/tiny-agents
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r-stats
description
82 statistical analysis methods in R — regression, survival, Bayesian, meta-analysis, causal inference, SEM, IRT, clinical trial design, and more. JSON spec driven, reproducible, with mandatory effect sizes and assumption checks. Use when: user asks for statistical analysis, hypothesis testing, regression, ANOVA, t-test, chi-square, correlation, survival analysis, Cox regression, meta-analysis, propensity score, causal inference, SEM, IRT, power analysis, sample size calculation, time series forecasting, mixed models, Bayesian analysis, ROC/AUC, agreement/reliability, zero-inflated models, penalized regression, LASSO, group sequential design, or mentions R packages like ggplot2, brms, survival, metafor, lavaan, glmnet, mice, lme4, gee, dagitty, tmle. Multilingual triggers — EN: statistics, regression, significance, predict; ZH: 统计分析, 回归, 检验, 预测, 显著性, 生存分析, 元分析, 贝叶斯; JA: 統計分析, 回帰, 検定, 予測; KO: 통계분석, 회귀, 검정; ES: análisis estadístico, regresión; FR: analyse statistique, régression; DE: st…
# OpenClaw R Stats ## When to Use User asks for any statistical analysis, hypothesis testing, group comparison, prediction, association, survival analysis, meta-analysis, causal inference, power/sample size, or mentions R statistical packages. ## What This Skill Does NOT Do - Claim causality from observational data (use "associated with") - Run large exploratory fishing without clear user intent - Silently ignore assumption violations - Report only p-values (always include effect sizes and CIs) ## Pre-Flight (Mandatory) 1. Confirm dataset exists and is readable 2. Run schema inspection: `bash {baseDir}/scripts/run-rstats.sh schema --data <path>` 3. Report: rows, columns, types, missing values 4. If missing > 5%, warn. If n < 30, warn small sample. ## Environment Setup First time or errors: `bash {baseDir}/scripts/run-rstats.sh doctor` Install by profile (only when needed): | Profile | Script | Methods | |---------|--------|---------| | Core | `install-core.R` | t-test, regression, ANOVA, chi-sq | | Survival | `install-survival.R` | KM, Cox, competing risks, RMST | | Missing | `install-missing.R` | MICE, MCAR test | | Mixed | `install-mixed.R` | LMM, GLMM, GEE, ICC | | Bayes | `install-bayes.R` | brms, Bayes factors | | Causal | `install-causal.R` | PSM, IPTW, IV, DiD, RDD, TMLE | | Meta | `install-meta.R` | meta-analysis, NMA | | SEM | `install-sem.R` | SEM, CFA, lavaan | | Diagnostic | `install-diagnostic.R` | ROC, kappa, alpha | | Advanced | `install-advanced.R` | GAM, quantile, zero-inflated | | Power | `install-power.R` | power/sample size | ## Workflow 1. Determine analysis type (see references/METHOD_TABLE.md) 2. Inspect dataset schema 3. Build JSON spec: ```json { "dataset_path": "<path>", "analysis_type": "<type>", "outcome": "<column>", "predictors": ["<col1>"], "hypothesis": "<plain language>", "alpha": 0.05, "seed": 42, "output_dir": "<path>" } ``` 4. Save as .json, run: `bash {baseDir}/scripts/run-rstats.sh analyze --spec <path>` 5. Read summary.json + report.md 6. Present: Summary → Statistics → Interpretation → Plots → Assumptions → Caveats ## Analysis Selection For the complete 82-method table with user intent mapping, see **references/METHOD_TABLE.md**. Quick lookup — most common: | Intent | analysis_type | |--------|--------------| | Compare 2 groups | `ttest` or `wilcoxon` | | Compare 3+ groups | `anova` or `kruskal` | | Categorical association | `chisq` or `fisher` | | Predict continuous | `linear_regression` | | Predict binary | `logistic_regression` | | Survival curves | `kaplan_meier` | | Survival regression | `cox_regression` | | Meta-analysis | `meta_analysis` | | Causal effect | `propensity_match` or `did` | | Power/sample size | `power_analysis` | ## Automatic Method Switching - Non-normal + n < 30 → `wilcoxon` over `ttest` - Unequal variance → Welch t-test (`equal_var: false`) - Expected cells < 5 → `fisher` over `chisq` - Overdispersion in Poisson → suggest negative binomial - Heteroscedastic residuals → robust SE warning ## Reporting Rules (Non-Negotiable) Every analysis MUST include: - Sample size (n) and missing data handling - Method name and rationale - Point estimates with confidence intervals - Effect sizes (Cohen's d, η², R², OR, HR, etc.) - Assumption check results - Limitations Language: "associated with" / "evidence suggests" — NEVER "proves" / "causes" ## Spec Field Reference See **references/SPEC_REFERENCE.md** for required/optional fields per analysis_type.
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