刘胜胜/statsmodels

About Statsmodels

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.

Documentation

The documentation for the latest release is at

https://www.statsmodels.org/stable/

The documentation for the development version is at

https://www.statsmodels.org/dev/

Recent improvements are highlighted in the release notes

https://www.statsmodels.org/stable/release/version0.9.html

Backups of documentation are available at https://statsmodels.github.io/stable/ and https://statsmodels.github.io/dev/.

Main Features

Linear regression models:
- Ordinary least squares
- Generalized least squares
- Weighted least squares
- Least squares with autoregressive errors
- Quantile regression
- Recursive least squares
Mixed Linear Model with mixed effects and variance components
GLM: Generalized linear models with support for all of the one-parameter exponential family distributions
Bayesian Mixed GLM for Binomial and Poisson
GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
Discrete models:
- Logit and Probit
- Multinomial logit (MNLogit)
- Poisson and Generalized Poisson regression
- Negative Binomial regression
- Zero-Inflated Count models
RLM: Robust linear models with support for several M-estimators.
Time Series Analysis: models for time series analysis
- Complete StateSpace modeling framework
  - Seasonal ARIMA and ARIMAX models
  - VARMA and VARMAX models
  - Dynamic Factor models
  - Unobserved Component models
- Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
- Univariate time series analysis: AR, ARIMA
- Vector autoregressive models, VAR and structural VAR
- Vector error correction modle, VECM
- exponential smoothing, Holt-Winters
- Hypothesis tests for time series: unit root, cointegration and others
- Descriptive statistics and process models for time series analysis
Survival analysis:
- Proportional hazards regression (Cox models)
- Survivor function estimation (Kaplan-Meier)
- Cumulative incidence function estimation
Multivariate:
- Principal Component Analysis with missing data
- Factor Analysis with rotation
- MANOVA
- Canonical Correlation
Nonparametric statistics: Univariate and multivariate kernel density estimators
Datasets: Datasets used for examples and in testing
Statistics: a wide range of statistical tests
- diagnostics and specification tests
- goodness-of-fit and normality tests
- functions for multiple testing
- various additional statistical tests
Imputation with MICE, regression on order statistic and Gaussian imputation
Mediation analysis
Graphics includes plot functions for visual analysis of data and model results
I/O
- Tools for reading Stata .dta files, but pandas has a more recent version
- Table output to ascii, latex, and html
Miscellaneous models
Sandbox: statsmodels contains a sandbox folder with code in various stages of developement and testing which is not considered "production ready". This covers among others
- Generalized method of moments (GMM) estimators
- Kernel regression
- Various extensions to scipy.stats.distributions
- Panel data models
- Information theoretic measures