scikits.statsmodels 0.3.1

Statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation of statistical models.<br /><br />Main Features<br /><br /> regression: Generalized least squares (including weighted least squares and least squares with autoregressive errors), ordinary least squares.<br /> glm: Generalized linear models with support for all of the one-parameter exponential family distributions.<br /> discrete choice models: Poisson, probit, logit, multinomial logit<br /> rlm: Robust linear models with support for several M-estimators.<br /> tsa: Time series analysis models, including ARMA, AR, VAR<br /> nonparametric : (Univariate) kernel density estimators<br /> datasets: Datasets to be distributed and used for examples and in testing.<br /> PyDTA: Tools for reading Stata .dta files into numpy arrays.<br /> stats: a wide range of statistical tests<br /> sandbox: There is also a sandbox which contains code for generalized additive models (untested), mixed effects models, cox proportional hazards model (both are untested and still dependent on the nipy formula framework), generating descriptive statistics, and printing table output to ascii, latex, and html. There is also experimental code for systems of equations regression, time series models, panel data estimators and information theoretic measures. None of this code is considered "production ready".

Requirements:	No special requirements
Platforms:	*nix, Linux
Keyword:	Additive,  Arrays,  Distributed,  Effects,  Examples,  Generalized,  Mixed,  Numpy,  Pydta,  Range,  Reading,  Sandbox,  Stats,  Testing,  Tests,  Tools,  Untested
Users rating:	0/10