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Add py-spvcm 0.3.0

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Gibbs sampling for spatially-correlated variance-components

This is a package to estimate spatially-correlated variance components
models/varying intercept models. In addition to a general toolkit to conduct
Gibbs sampling in Python, the package also provides an interface to PyMC3 and
CODA.

WWW: https://github.com/pysal/spvcm
This commit is contained in:
Sunpoet Po-Chuan Hsieh 2021-01-03 19:57:48 +00:00
parent a969ae95d6
commit ff64a64035
Notes: svn2git 2021-03-31 03:12:20 +00:00 
svn path=/head/; revision=560049
4 changed files with 39 additions and 0 deletions
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1
math/Makefile
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@ -848,6 +848,7 @@
SUBDIR += py-splot SUBDIR += py-splot
SUBDIR += py-spot SUBDIR += py-spot
SUBDIR += py-spreg SUBDIR += py-spreg
SUBDIR += py-spvcm
SUBDIR += py-ssm SUBDIR += py-ssm
SUBDIR += py-statsmodels SUBDIR += py-statsmodels
SUBDIR += py-statsmodels010 SUBDIR += py-statsmodels010

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math/py-spvcm/Makefile Normal file
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@ -0,0 +1,27 @@
# Created by: Po-Chuan Hsieh <sunpoet@FreeBSD.org>
# $FreeBSD$
PORTNAME= spvcm
PORTVERSION= 0.3.0
CATEGORIES= math python
MASTER_SITES= CHEESESHOP
PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
MAINTAINER= sunpoet@FreeBSD.org
COMMENT= Fit spatial multilevel models and diagnose convergence
LICENSE= BSD3CLAUSE
RUN_DEPENDS= ${PYTHON_PKGNAMEPREFIX}libpysal>=0:science/py-libpysal@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}numpy>=0,1:math/py-numpy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}pandas>=0,1:math/py-pandas@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}scipy>=0:science/py-scipy@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}seaborn>=0:math/py-seaborn@${PY_FLAVOR} \
${PYTHON_PKGNAMEPREFIX}spreg>=0:math/py-spreg@${PY_FLAVOR}
USES= python:3.6+
USE_PYTHON= autoplist concurrent distutils
NO_ARCH= yes
.include <bsd.port.mk>

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math/py-spvcm/distinfo Normal file
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TIMESTAMP = 1609598753
SHA256 (spvcm-0.3.0.tar.gz) = ce331bd5d6bcb64a07c4393093f3978763cfc8764ad0737e1866f3905e6cceae
SIZE (spvcm-0.3.0.tar.gz) = 5724408

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math/py-spvcm/pkg-descr Normal file
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Gibbs sampling for spatially-correlated variance-components
This is a package to estimate spatially-correlated variance components
models/varying intercept models. In addition to a general toolkit to conduct
Gibbs sampling in Python, the package also provides an interface to PyMC3 and
CODA.
WWW: https://github.com/pysal/spvcm