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Add py-statsmodels010 0.10.2 (copied from py-statsmodels)
- Add PORTSCOUT
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parent
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commit
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Notes:
svn2git
2021-03-31 03:12:20 +00:00
svn path=/head/; revision=524057
@ -796,6 +796,7 @@
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SUBDIR += py-spectral
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SUBDIR += py-spot
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SUBDIR += py-statsmodels
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SUBDIR += py-statsmodels010
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SUBDIR += py-svgmath
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SUBDIR += py-sym
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SUBDIR += py-symcxx
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45
math/py-statsmodels010/Makefile
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45
math/py-statsmodels010/Makefile
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# Created by: Johannes Meixner <johannes@perceivon.net>
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# $FreeBSD$
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PORTNAME= statsmodels
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PORTVERSION= 0.10.2
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CATEGORIES= math python
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MASTER_SITES= CHEESESHOP
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PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
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PKGNAMESUFFIX= 010
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MAINTAINER= sunpoet@FreeBSD.org
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COMMENT= Complement to SciPy for statistical computations
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LICENSE= BSD3CLAUSE
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BUILD_DEPENDS= ${RUN_DEPENDS}
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RUN_DEPENDS= ${PYNUMPY} \
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${PYTHON_PKGNAMEPREFIX}pandas>=0.19:math/py-pandas@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}patsy>=0.4.0:math/py-patsy@${PY_FLAVOR} \
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${PYTHON_PKGNAMEPREFIX}scipy>=0.18:science/py-scipy@${PY_FLAVOR}
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USES= python shebangfix
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USE_PYTHON= autoplist concurrent cython distutils
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PORTDOCS= README.rst README_l1.txt
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PORTEXAMPLES= *
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PORTSCOUT= limit:^0\.10\.
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SHEBANG_GLOB= *.py
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OPTIONS_DEFINE= DOCS EXAMPLES
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post-install:
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${FIND} ${STAGEDIR}${PYTHON_SITELIBDIR} -name '*.so' -exec ${STRIP_CMD} {} +
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post-install-DOCS-on:
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${MKDIR} ${STAGEDIR}${DOCSDIR}
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${INSTALL_DATA} ${WRKSRC}/README.rst ${WRKSRC}/README_l1.txt ${STAGEDIR}${DOCSDIR}
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post-install-EXAMPLES-on:
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${MKDIR} ${STAGEDIR}${EXAMPLESDIR}
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cd ${WRKSRC}/examples && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR}
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.include <bsd.port.mk>
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math/py-statsmodels010/distinfo
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math/py-statsmodels010/distinfo
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TIMESTAMP = 1575793969
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SHA256 (statsmodels-0.10.2.tar.gz) = 9cd2194c6642a8754e85f9a6e6912cdf996bebf6ff715d3cc67f65dadfd37cc9
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SIZE (statsmodels-0.10.2.tar.gz) = 14065612
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24
math/py-statsmodels010/pkg-descr
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math/py-statsmodels010/pkg-descr
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Statsmodels is a Python package that provides a complement to scipy for
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statistical computations including descriptive statistics and estimation and
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inference for statistical models.
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Main Features:
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* linear regression models: GLS (including WLS and LS aith AR errors) and OLS.
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* glm: Generalized linear models with support for all of the one-parameter
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exponential family distributions.
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* discrete: regression with discrete dependent variables, including Logit,
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Probit, MNLogit, Poisson, based on maximum likelihood estimators
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* rlm: Robust linear models with support for several M-estimators.
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* tsa: models for time series analysis - univariate: AR, ARIMA; multivariate:
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VAR and structural VAR
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* nonparametric: (Univariate) kernel density estimators
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* datasets: Datasets to be distributed and used for examples and in testing.
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* stats: a wide range of statistical tests, diagnostics and specification tests
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* iolib: Tools for reading Stata .dta files into numpy arrays, printing table
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output to ascii, latex, and html
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* miscellaneous models
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* sandbox: statsmodels contains a sandbox folder with code in various stages of
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* developement and testing which is not considered "production ready", including
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Mixed models, GARCH and GMM estimators, kernel regression, panel data models.
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WWW: https://github.com/statsmodels/statsmodels
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