Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
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Exceptional statistical modeling skill with comprehensive coverage of statsmodels capabilities. The description clearly delineates when to use this skill vs alternatives. SKILL.md provides extensive quick-start examples, detailed workflow guidance, and best practices across linear models, GLMs, discrete choice, and time series. The structure is well-organized with a logical progression from quick starts to advanced topics, supported by five detailed reference files for deep dives. Code examples are production-ready with proper error handling and diagnostics. The skill addresses a genuinely complex domain (statistical inference, model diagnostics, time series) where a CLI agent would struggle with parameter selection, assumption testing, and interpretation without guidance. Minor improvement possible in structure organization (some sections are lengthy), but the comprehensive nature justifies the depth. The reference file system effectively prevents clutter while maintaining accessibility.
K-Dense-AI
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