mirror/freebsd-ports
1
0
Fork 0
mirror of https://git.FreeBSD.org/ports.git synced 2024-11-18 00:10:04 +00:00
Code Issues Releases Activity

math/py-statsmodels: Update WWW and pkg-descr

Browse Source
This commit is contained in:
Po-Chuan Hsieh 2024-05-16 14:18:23 +08:00
parent a4f6b8f45c
commit ea0d963ab4
No known key found for this signature in database
GPG Key ID: 9A4BD10F002DD04B
2 changed files with 27 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
math/py-statsmodels/Makefile
View File

@ -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

43
math/py-statsmodels/pkg-descr
View File

@ -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