May 26, 2018
Complement to SciPy for statistical computations
Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models.
- linear regression models GLS including WLS and LS aith AR errors and OLS.
- glm Generalized linear models with support for all of the one-parameter exponential family distributions.
- discrete regression with discrete dependent variables, including Logit, Probit, MNLogit, Poisson, based on maximum likelihood estimators
- rlm Robust linear models with support for several M-estimators.
- tsa models for time series analysis - univariate AR, ARIMA; multivariate VAR and structural VAR
- nonparametric Univariate kernel density estimators
- datasets Datasets to be distributed and used for examples and in testing.
- stats a wide range of statistical tests, diagnostics and specification tests
- iolib Tools for reading Stata .dta files into numpy arrays, printing 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”, including Mixed models, GARCH and GMM estimators, kernel regression, panel data models.