Eda
Exploratory data analysis reference for Bayesian modeling — operational EDA procedures (data semantics audit, quality checks, timestamp handling, visualization, modeling handoff) plus a diagnostic test library indexed by data shape.From its SKILL.md
npx -y skills add sunxd3/bayesian-statistician-plugin --skill edaAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.3 KB, as published. Nobody here has run it
Exploratory Data Analysis
Reference for performing EDA in service of Bayesian modeling. Read references on demand based on what the data and current step require.
Key Practices
- Confounder-aware comparisons. Any claimed association or "effect" must condition on the strongest available confounder — typically the most granular temporal unit, or the variable with the highest marginal association to both predictor and outcome. Never report unconditional means as effects. Always report sample size per arm.
Process references
Operational checklists and procedures for each step of the EDA workflow:
references/process/data-semantics-audit.md— understanding what columns mean (scientific domain, column roles, granularity, sparsity, encoding sanity, suspicious patterns)references/process/standardization.md— mechanical cleanup driven by the audit (snake_case names, NaN harmonization, type coercion, categorical normalization) that produces the canonical cleaned datasetreferences/process/data-quality-checks.md— missingness, duplicates, invalid values, type issuesreferences/process/timestamp-handling.md— parsing timestamps, frequency inference, gaps, panel coveragereferences/process/visualization.md— plot quantity, structure, and documentation requirementsreferences/process/modeling-handoff.md— variance decomposition, residual analysis, competing structural hypotheses, dependence classification, likelihood mapping, modeling-ready recommendations
For the HTML report format (design, palette, fonts, skeleton template), see artifact-guidelines > references/html-report. For log format, see artifact-guidelines > references/markdown-report.
Test references
Statistical diagnostic tests organized by data shape. Load only what your data needs:
references/tests/distribution.md— normality tests (Shapiro-Wilk, Jarque-Bera, Anderson-Darling), QQ-plot patterns, formal goodness-of-fit (KS, AD)references/tests/regression.md— heteroscedasticity (Breusch-Pagan, White, Goldfeld-Quandt), multicollinearity (VIF, condition number), specification (Ramsey RESET), influence diagnostics (leverage, Cook's D, DFBETAS, Studentized residuals, Mahalanobis)references/tests/time-series.md— stationarity (ADF, KPSS), autocorrelation (Durbin-Watson, Ljung-Box), ACF/PACF patterns, ARCH effectsreferences/tests/count.md— overdispersion (Cameron-Trivedi, dispersion parameter), zero-inflation, Vuong test for non-nested modelsreferences/tests/missing-and-hierarchical.md— MCAR/MAR/MNAR mechanisms, Little's test, ICC, design effect
Key Thresholds Summary
| Test | Threshold | Interpretation |
|---|---|---|
| Shapiro-Wilk W | > 0.95 | Approximately normal |
| VIF | > 10 | Severe multicollinearity |
| Durbin-Watson | 1.5-2.5 | No serious autocorrelation |
| ADF critical | -2.86 | 5% significance |
| Cook's D | > 1 | High influence |
| ICC | > 0.05 | Use multilevel model |
| Dispersion φ | > 2 | Overdispersion |