Date: Wed, 13 Nov 2013 18:16:09 +0200 (EET) From: Johannes Jost Meixner <xmj@chaot.net> To: FreeBSD-gnats-submit@freebsd.org Subject: ports/183932: [NEW PORT] math/py-statsmodels: Complement to SciPy for statistical computations Message-ID: <61554472.enqueue@mx12.chaot.net> Resent-Message-ID: <201311131620.rADGK27H016051@freefall.freebsd.org>
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>Number: 183932 >Category: ports >Synopsis: [NEW PORT] math/py-statsmodels: Complement to SciPy for statistical computations >Confidential: no >Severity: non-critical >Priority: low >Responsible: freebsd-ports-bugs >State: open >Quarter: >Keywords: >Date-Required: >Class: change-request >Submitter-Id: current-users >Arrival-Date: Wed Nov 13 16:20:01 UTC 2013 >Closed-Date: >Last-Modified: >Originator: Johannes Jost Meixner >Release: FreeBSD 11.0-CURRENT amd64 >Organization: >Environment: System: FreeBSD mx12.chaot.net 11.0-CURRENT FreeBSD 11.0-CURRENT #3: Thu Nov 7 16:08:22 EET >Description: 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 Generated with FreeBSD Port Tools 0.99_8 (mode: new) >How-To-Repeat: >Fix: --- .shar begins here --- # This is a shell archive. Save it in a file, remove anything before # this line, and then unpack it by entering "sh file". 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$FreeBSD$ X XPORTNAME= statsmodels XPORTVERSION= 0.5.0 XCATEGORIES= math python XMASTER_SITES= CHEESESHOP XPKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} X XMAINTAINER= xmj@chaot.net XCOMMENT= Complement to SciPy for statistical computations X XLICENSE= BSD X XBUILD_DEPENDS= ${PYNUMPY} \ X ${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy \ X ${PYTHON_PKGNAMEPREFIX}pandas>0:${PORTSDIR}/math/py-pandas \ X ${PYTHON_PKGNAMEPREFIX}patsy>0:${PORTSDIR}/math/py-patsy XRUN_DEPENDS= ${PYNUMPY} \ X ${PYTHON_PKGNAMEPREFIX}scipy>0:${PORTSDIR}/science/py-scipy \ X ${PYTHON_PKGNAMEPREFIX}pandas>0:${PORTSDIR}/math/py-pandas \ X ${PYTHON_PKGNAMEPREFIX}patsy>0:${PORTSDIR}/math/py-patsy X XOPTIONS_DEFINE= DOCS EXAMPLES XDOCSDIR= ${PREFIX}/share/doc/py-${PORTNAME} XEXAMPLESDIR= ${PREFIX}/share/doc/py-${PORTNAME} X XUSE_PYTHON= yes XUSE_PYDISTUTILS= yes XPLIST_SUB= PYDISTUTILS_EGGINFO=${PYDISTUTILS_EGGINFO} XPYTHON_P3K_PLIST_HACK= yes XPYDISTUTILS_NOEGGINFO= yes XPORTDOCS= * .buildinfo XPORTEXAMPLES= * X X.include <bsd.port.options.mk> X X.include <bsd.port.pre.mk> X X.if ${PORT_OPTIONS:MDOCS} X XBUILD_DEPENDS+= sphinx-build:${PORTSDIR}/textproc/py-sphinx \ X ${PYTHON_PKGNAMEPREFIX}matplotlib>0:${PORTSDIR}/math/py-matplotlib \ X ipython:${PORTSDIR}/devel/ipython X XPYDISTUTILS_BUILD_TARGET+= build_sphinx X.endif X Xpost-install: X ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/nonparametric/_smoothers_lowess.so X ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/nonparametric/linbin.so X ${STRIP_CMD} ${STAGEDIR}${PYTHON_SITELIBDIR}/${PORTNAME}/tsa/kalmanf/kalman_loglike.so X.if ${PORT_OPTIONS:MDOCS} X ${MKDIR} ${STAGEDIR}${DOCSDIR} X ${INSTALL_DATA} ${WRKSRC}/README.txt ${WRKSRC}/README_l1.txt ${STAGEDIR}${DOCSDIR} X cd ${WRKSRC}/build/sphinx/html && ${COPYTREE_SHARE} . ${STAGEDIR}${DOCSDIR} X.endif X ${MKDIR} ${STAGEDIR}${EXAMPLESDIR} X cd ${WRKSRC}/examples && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR} X X.include <bsd.port.post.mk> f50f25f2c5cea5ebf7bf79d2440304ec echo x - py-statsmodels/pkg-descr sed 's/^X//' >py-statsmodels/pkg-descr << 'ea61e400765cdc580653dc2cccb08a3a' XStatsmodels is a Python package that provides a complement to scipy for Xstatistical computations including descriptive statistics and estimation and Xinference for statistical models. X XMain Features: X* linear regression models: GLS (including WLS and LS aith AR errors) and OLS. X* glm: Generalized linear models with support for all of the one-parameter X exponential family distributions. X* discrete: regression with discrete dependent variables, including Logit, X Probit, MNLogit, Poisson, based on maximum likelihood estimators X* rlm: Robust linear models with support for several M-estimators. X* tsa: models for time series analysis - univariate: AR, ARIMA; multivariate: X VAR and structural VAR X* nonparametric: (Univariate) kernel density estimators X* datasets: Datasets to be distributed and used for examples and in testing. X* stats: a wide range of statistical tests, diagnostics and specification tests X* iolib: Tools for reading Stata .dta files into numpy arrays, printing table X output to ascii, latex, and html X* miscellaneous models X* sandbox: statsmodels contains a sandbox folder with code in various stages of X* developement and testing which is not considered "production ready", including X Mixed models, GARCH and GMM estimators, kernel regression, panel data models. X XWWW: https://www.github.com/statsmodels/statsmodels ea61e400765cdc580653dc2cccb08a3a echo x - py-statsmodels/distinfo sed 's/^X//' >py-statsmodels/distinfo << 'fbfed42bca1ebbf945deaeb881244048' XSHA256 (statsmodels-0.5.0.tar.gz) = 65398518bdd414c712362738e61d34ee5ec07b4c084bba17c65af5f20ae109d0 XSIZE (statsmodels-0.5.0.tar.gz) = 5519668 fbfed42bca1ebbf945deaeb881244048 exit --- .shar ends here --- >Release-Note: >Audit-Trail: >Unformatted:
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