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statsmodels

statsmodels
open source / Overall score: 4.2(strong)

Python library for statistical modeling and inference: linear, generalized linear, mixed and robust regression, discrete-choice models, survival analysis, multivariate methods and a large time-series suite from ARIMA and SARIMAX to state-space, VAR and Markov-switching models. It reports standard errors, tests and diagnostics alongside every fit. Maintained by the statsmodels developers.

Openness

5 low confidence
5.0
license
BSD-3-Clause(OSI
source
public(github.com/statsmodels/statsmodels)
core features withheld
no — no paid tier or commercial edition found

The license file is the three-clause BSD text under the statsmodels developers' copyright, and the README names it Modified BSD. The library builds whole from the public repository, and neither the README nor the package lists a paid tier, a hosted service or a commercial license; nothing states this outright, so the reading is that no commercial offering exists to hold anything back for.

Adoption

5 high confidence
5.0

Measured on monthly PyPI downloads of the statsmodels package.

Capability

3 high confidence
3.0

statsmodels is a toolkit of many model families for inference rather than prediction alone, and its time-series suite is one of the widest in Python. Like scikit-learn it leaves model choice to the analyst.

  • https://raw.githubusercontent.com/statsmodels/statsmodels/HEAD/README.md recorded 2026-09-26

    README "Main Features": linear regression models, mixed linear model, GLM, Bayesian mixed GLM, GEE, discrete models, RLM, time series analysis (StateSpace, SARIMAX, VARMAX, dynamic factor, Markov switching, AR/ARIMA, VAR/SVAR, VECM, Holt-Winters), survival analysis, multivariate, nonparametric, statistical tests, MICE.

  • https://www.statsmodels.org/stable/user-guide.html recorded 2026-09-26

    User Guide index: linear regression, GLM, GEE, GAM, robust linear models, linear mixed effects, discrete dependent variables, ANOVA, time series analysis, state space methods, vector autoregressions, survival and duration analysis, nonparametric methods, GMM, multivariate statistics.

Verified 2026-09-26