# statsmodels/statsmodels

Statsmodels: statistical modeling and econometrics in Python

Repository: https://github.com/statsmodels/statsmodels
Canonical: https://ross.abutalabs.com/products/statsmodels
Homepage: http://www.statsmodels.org/devel/
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: python, statistics, econometrics, data-analysis, generalized-linear-models, timeseries-analysis, regression-models, count-model, data-science, forecasting, hypothesis-testing, prediction, robust-estimation
Last push: 2026-08-26T16:44:27+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 67, longevity 100
- inputs: {"age_days": 5561, "days_push": 7, "days_rel": 6, "gap_med": 207.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11593, forks 3569 (observed 2026-08-28T04:10:48.805814+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: data-science, math, analytics
- domain: data-science, time-series, analytics
- platform: python, cross-platform
- tags: statistics, econometrics, regression, time-series-analysis, hypothesis-testing, forecasting, glm, data-analysis

## Member repositories
- statsmodels/statsmodels (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:48.805814+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:15:29.480139+00:00, confidence not recorded.
  - readme: https://github.com/statsmodels/statsmodels (fetched 2026-08-28T04:10:48.805814+00:00, sha 36386e22e586)
  - homepage: http://www.statsmodels.org/devel/ (fetched 2026-08-29T08:13:58.086670+00:00, sha e0cecb536768)
  - site_page: https://www.statsmodels.org/devel/install.html (fetched 2026-08-29T08:13:58.096309+00:00, sha ee8dcd8bc768)
  - site_page: https://www.statsmodels.org/devel/about.html (fetched 2026-08-29T08:13:58.098173+00:00, sha ea5631011476)
  - site_page: https://www.statsmodels.org/devel/gettingstarted.html (fetched 2026-08-29T08:13:58.099837+00:00, sha f129ccd338e5)
  - site_page: https://www.statsmodels.org/devel/api.html (fetched 2026-08-29T08:13:58.101711+00:00, sha d96cbcb238a1)
- Data as of 2026-08-30T08:39:29.467469+00:00.