statsmodels/statsmodels

Health v2 · maintenance only

Adoption not part of the score

11593 stars · 3569 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Statsmodels is a Python library providing classes and functions for estimating statistical models, conducting hypothesis tests, and exploring data. It covers linear and generalized linear regression, discrete and count models, robust estimation, and a comprehensive time series analysis framework.

Use cases

• fit regression models in python
• run hypothesis tests on data
• forecast time series with ARIMA
• estimate econometric models
• perform logistic regression with statistical inference
• analyze seasonal time series data
• fit mixed effects models
• explore data with descriptive statistics

When to choose

• you need statistically rigorous model estimation with p-values, confidence intervals, and diagnostics
• you want R-style formula interfaces with pandas DataFrames
• you need econometric or time series models like ARIMA, VAR, or state space models
• you want results verified against R, Stata, or SAS

When to avoid

• you need machine learning prediction pipelines rather than statistical inference - scikit-learn is a better fit
• you need deep learning or GPU-accelerated modeling
• you need big-data distributed computation - statsmodels is in-memory and single-machine

Facets

library · maturity stable

data-science math analytics data-science time-series analytics python cross-platform statistics econometrics regression time-series-analysis hypothesis-testing forecasting glm data-analysis

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