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mirror of https://git.FreeBSD.org/ports.git synced 2025-01-10 07:04:03 +00:00

Add py-statsmodels010 0.10.2 (copied from py-statsmodels)

- Add PORTSCOUT
This commit is contained in:
Sunpoet Po-Chuan Hsieh 2020-01-25 18:17:55 +00:00
parent f5f481bd57
commit 76bfc9a9aa
Notes: svn2git 2021-03-31 03:12:20 +00:00
svn path=/head/; revision=524057
4 changed files with 73 additions and 0 deletions

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@ -796,6 +796,7 @@
SUBDIR += py-spectral
SUBDIR += py-spot
SUBDIR += py-statsmodels
SUBDIR += py-statsmodels010
SUBDIR += py-svgmath
SUBDIR += py-sym
SUBDIR += py-symcxx

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# Created by: Johannes Meixner <johannes@perceivon.net>
# $FreeBSD$
PORTNAME= statsmodels
PORTVERSION= 0.10.2
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
PKGNAMESUFFIX= 010
MAINTAINER= sunpoet@FreeBSD.org
COMMENT= Complement to SciPy for statistical computations
LICENSE= BSD3CLAUSE
BUILD_DEPENDS= ${RUN_DEPENDS}
RUN_DEPENDS= ${PYNUMPY} \
${PYTHON_PKGNAMEPREFIX}pandas>=0.19:math/py-pandas@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}patsy>=0.4.0:math/py-patsy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scipy>=0.18:science/py-scipy@${PY_FLAVOR}
USES= python shebangfix
USE_PYTHON= autoplist concurrent cython distutils
PORTDOCS= README.rst README_l1.txt
PORTEXAMPLES= *
PORTSCOUT= limit:^0\.10\.
SHEBANG_GLOB= *.py
OPTIONS_DEFINE= DOCS EXAMPLES
post-install:
${FIND} ${STAGEDIR}${PYTHON_SITELIBDIR} -name '*.so' -exec ${STRIP_CMD} {} +
post-install-DOCS-on:
${MKDIR} ${STAGEDIR}${DOCSDIR}
${INSTALL_DATA} ${WRKSRC}/README.rst ${WRKSRC}/README_l1.txt ${STAGEDIR}${DOCSDIR}
post-install-EXAMPLES-on:
${MKDIR} ${STAGEDIR}${EXAMPLESDIR}
cd ${WRKSRC}/examples && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR}
.include <bsd.port.mk>

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TIMESTAMP = 1575793969
SHA256 (statsmodels-0.10.2.tar.gz) = 9cd2194c6642a8754e85f9a6e6912cdf996bebf6ff715d3cc67f65dadfd37cc9
SIZE (statsmodels-0.10.2.tar.gz) = 14065612

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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://github.com/statsmodels/statsmodels