Python的统计建模和计量经济学工具包：Statsmodels
jopen
10年前
Statsmodels是Python的统计建模和计量经济学工具包,包括一些描述统计、统计模型估计和推断。
Main Features
- linear regression models: Generalized least squares (including weighted least squares and least squares with autoregressive errors), ordinary least squares.
- 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 time series analysis: AR, ARIMA - vector autoregressive models, VAR and structural VAR - descriptive statistics and process models for time series analysis
- 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 - goodness-of-fit and normality tests - functions for multiple testing - various additional statistical 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". This covers among others Mixed (repeated measures) Models, GARCH models, general method of moments (GMM) estimators, kernel regression, various extensions to scipy.stats.distributions, panel data models, generalized additive models and information theoretic measures.