Statsmodels

When you need to fit OLS, logistic regression, ARIMA models, or any serious statistical analysis with proper inference tables, this is your tool. It gives you coefficient estimates, p-values, confidence intervals, and all the diagnostic tests you'd expect for publication-quality work. The skill covers linear models, GLMs across different distributions, discrete choice models, and time series. It's heavier than scikit-learn but that's the point: you get rigorous econometric analysis with residual diagnostics, heteroskedasticity tests, and marginal effects. If you're doing statistical modeling where the inference matters as much as the predictions, this is the standard library for it.

skills.sh installs: 2.3K
GitHub stars: 56.6K
last updated: Aug 21, 2026

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Install

$ npx -y skills add k-dense-ai/scientific-agent-skills --skill statsmodels --agent claude-code

Installs into .claude/skills of the current project.

Files

Statsmodels: Statistical Modeling and Econometrics

Overview

Statsmodels provides estimation, inference and diagnostics for regression, time series and econometric models. A successful fit establishes numerical execution; causal identification, calibrated uncertainty and model adequacy require a defensible study design and assumptions.

Current Compatibility

Reviewed against statsmodels 0.15.0 (released August 27, 2026). Native checks used Python 3.13, NumPy 2.5.3, SciPy 1.18.1, pandas 3.0.6, matplotlib 3.11.2 and scikit-learn 1.9.1. Install in a dedicated environment:

uv pip install statsmodels==0.15.0 numpy==2.5.3 scipy==1.18.1 pandas==3.0.6 matplotlib==3.11.2 scikit-learn==1.9.1

Use statsmodels.api and statsmodels.formula.api for stable high-level imports, and direct module imports when examples require newer or specialized classes such as HurdleCountModel.

SKILL.md on GitHub