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| author | Po-Chuan Hsieh <sunpoet@FreeBSD.org> | 2024-05-16 06:18:23 +0000 |
|---|---|---|
| committer | Po-Chuan Hsieh <sunpoet@FreeBSD.org> | 2024-05-16 06:22:08 +0000 |
| commit | ea0d963ab4038873ebb104fdea0a58c747fae06c (patch) | |
| tree | 2b9b7469dbacf3d440000c53b4875dbc6d2e3b0d | |
| parent | a4f6b8f45c7da29c36ff97605d8a80ac9481d0eb (diff) | |
| download | ports-ea0d963ab4038873ebb104fdea0a58c747fae06c.tar.gz ports-ea0d963ab4038873ebb104fdea0a58c747fae06c.zip | |
math/py-statsmodels: Update WWW and pkg-descr
| -rw-r--r-- | math/py-statsmodels/Makefile | 3 | ||||
| -rw-r--r-- | math/py-statsmodels/pkg-descr | 43 |
2 files changed, 27 insertions, 19 deletions
diff --git a/math/py-statsmodels/Makefile b/math/py-statsmodels/Makefile index 669980fc9f70..d6cbc65701a2 100644 --- a/math/py-statsmodels/Makefile +++ b/math/py-statsmodels/Makefile @@ -6,7 +6,8 @@ PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} MAINTAINER= sunpoet@FreeBSD.org COMMENT= Complement to SciPy for statistical computations -WWW= https://github.com/statsmodels/statsmodels +WWW= https://www.statsmodels.org/stable/ \ + https://github.com/statsmodels/statsmodels LICENSE= BSD3CLAUSE LICENSE_FILE= ${WRKSRC}/LICENSE.txt diff --git a/math/py-statsmodels/pkg-descr b/math/py-statsmodels/pkg-descr index 5e5e83d80f62..a87433b76fc1 100644 --- a/math/py-statsmodels/pkg-descr +++ b/math/py-statsmodels/pkg-descr @@ -1,22 +1,29 @@ -Statsmodels is a Python package that provides a complement to scipy for +statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. Main Features: -* 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. +- Linear regression models +- 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 +- RLM: Robust linear models with support for several M-estimators. +- Time Series Analysis: models for time series analysis +- Survival analysis +- Multivariate +- Nonparametric statistics: Univariate and multivariate kernel density + estimators +- Datasets: Datasets used for examples and in testing +- Statistics: a wide range of 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 +- Miscellaneous models +- Sandbox: statsmodels contains a sandbox folder with code in various stages of + development and testing which is not considered "production ready". This + covers among others |