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math/py-statsmodels: Complement to SciPy for statistical computations

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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.

WWW: https://www.github.com/statsmodels/statsmodels

PR:		ports/183932
Submitted by:	Johannes Jost Meixner <xmj chaot.net>
This commit is contained in:
William Grzybowski 2013-11-22 12:40:09 +00:00
parent ce8c9cb003
commit 01725b54b2
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=334582
5 changed files with 2208 additions and 0 deletions
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1
math/Makefile
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@ -568,6 +568,7 @@
SUBDIR += py-pymc
SUBDIR += py-roman
SUBDIR += py-scientific
SUBDIR += py-statsmodels
SUBDIR += py-svgmath
SUBDIR += py-symeig
SUBDIR += py-sympy

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math/py-statsmodels/Makefile Normal file
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@ -0,0 +1,58 @@
# Created by: Johannes Meixner <xmj@chaot.net>
# $FreeBSD$
PORTNAME= statsmodels
PORTVERSION= 0.5.0
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= xmj@chaot.net
COMMENT= Complement to SciPy for statistical computations
LICENSE= BSD
BUILD_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy \
${PYTHON_PKGNAMEPREFIX}pandas>0:${PORTSDIR}/math/py-pandas \
${PYTHON_PKGNAMEPREFIX}patsy>0:${PORTSDIR}/math/py-patsy
RUN_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy \
${PYTHON_PKGNAMEPREFIX}pandas>0:${PORTSDIR}/math/py-pandas \
${PYTHON_PKGNAMEPREFIX}patsy>0:${PORTSDIR}/math/py-patsy
OPTIONS_DEFINE= DOCS EXAMPLES
DOCSDIR= ${PREFIX}/share/doc/py-${PORTNAME}
EXAMPLESDIR= ${PREFIX}/share/doc/py-${PORTNAME}
USE_PYTHON= yes
USE_PYDISTUTILS= yes
PLIST_SUB= PYDISTUTILS_EGGINFO=${PYDISTUTILS_EGGINFO}
PYTHON_P3K_PLIST_HACK= yes
PYDISTUTILS_NOEGGINFO= yes
PORTDOCS= * .buildinfo
PORTEXAMPLES= *
.include <bsd.port.options.mk>
.if ${PORT_OPTIONS:MDOCS}
BUILD_DEPENDS+= sphinx-build:${PORTSDIR}/textproc/py-sphinx \
${PYTHON_PKGNAMEPREFIX}matplotlib>0:${PORTSDIR}/math/py-matplotlib \
ipython:${PORTSDIR}/devel/ipython
PYDISTUTILS_BUILD_TARGET+= build_sphinx
.endif
post-install:
${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/nonparametric/_smoothers_lowess.so
${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/nonparametric/linbin.so
${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/tsa/kalmanf/kalman_loglike.so
.if ${PORT_OPTIONS:MDOCS}
${MKDIR} ${STAGEDIR}${DOCSDIR}
${INSTALL_DATA} ${WRKSRC}/README.txt ${WRKSRC}/README_l1.txt ${STAGEDIR}${DOCSDIR}
cd ${WRKSRC}/build/sphinx/html && ${COPYTREE_SHARE} . ${STAGEDIR}${DOCSDIR}
.endif
${MKDIR} ${STAGEDIR}${EXAMPLESDIR}
cd ${WRKSRC}/examples && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR}
.include <bsd.port.mk>

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math/py-statsmodels/distinfo Normal file
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SHA256 (statsmodels-0.5.0.tar.gz) = 65398518bdd414c712362738e61d34ee5ec07b4c084bba17c65af5f20ae109d0
SIZE (statsmodels-0.5.0.tar.gz) = 5519668

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math/py-statsmodels/pkg-descr Normal file
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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.
WWW: https://www.github.com/statsmodels/statsmodels

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math/py-statsmodels/pkg-plist Normal file
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