svn commit: r560045 - in head/math: . py-spglm

[Sunpoet Po-Chuan Hsieh]

Author: sunpoet
Date: Sun Jan  3 19:57:28 2021
New Revision: 560045
URL: https://svnweb.freebsd.org/changeset/ports/560045

Log:
  Add py-spglm 1.0.8
  
  This module is an adaptation of a portion of GLM functionality from the
  Statsmodels package, this it has been simplified and customized for the purposes
  of serving as the base for several other PySAL modules, namely SpInt and GWR.
  Currently, it supports the estimation of Gaussian, Poisson, and Logistic
  regression using only iteratively weighted least squares estimation (IWLS). One
  of the large differences this module and the functions avaialble in the
  Statsmodels package is that the custom IWLS routine is fully sparse compatible,
  which was necesary for the very sparse design matrices that arise in constrained
  spatial interaction models. The somewhat limited functionality and computation
  of only a subset of GLM diagnostics also decreases the computational overhead.
  Another difference is that this module also supports the estimation of
  QuasiPoisson models. One caveat is that this custom IWLS routine currently
  generates estimates by directly solves the least squares normal equations rather
  than using a more robust method like the pseudo inverse. For more robust
  estimation of ill conditioned data and a fuller GLM framework we suggest using
  the original GLM functionality from Statsmodels.
  
  WWW: https://github.com/pysal/spglm

Added:
  head/math/py-spglm/
  head/math/py-spglm/Makefile   (contents, props changed)
  head/math/py-spglm/distinfo   (contents, props changed)
  head/math/py-spglm/pkg-descr   (contents, props changed)
Modified:
  head/math/Makefile

Modified: head/math/Makefile
==============================================================================
--- head/math/Makefile	Sun Jan  3 19:57:22 2021	(r560044)
+++ head/math/Makefile	Sun Jan  3 19:57:28 2021	(r560045)
@@ -843,6 +843,7 @@
     SUBDIR += py-simhash
     SUBDIR += py-snuggs
     SUBDIR += py-spectral
+    SUBDIR += py-spglm
     SUBDIR += py-spot
     SUBDIR += py-ssm
     SUBDIR += py-statsmodels

Added: head/math/py-spglm/Makefile
==============================================================================
--- /dev/null	00:00:00 1970	(empty, because file is newly added)
+++ head/math/py-spglm/Makefile	Sun Jan  3 19:57:28 2021	(r560045)
@@ -0,0 +1,25 @@
+# Created by: Po-Chuan Hsieh <sunpoet at FreeBSD.org>
+# $FreeBSD$
+
+PORTNAME=	spglm
+PORTVERSION=	1.0.8
+CATEGORIES=	math python
+MASTER_SITES=	CHEESESHOP
+PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER=	sunpoet at FreeBSD.org
+COMMENT=	Sparse generalize linear models
+
+LICENSE=	BSD3CLAUSE
+
+RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}libpysal>=4.0.0:science/py-libpysal@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}numpy>=1.3,1:math/py-numpy@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}scipy>=0.11:science/py-scipy@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}spreg>=1.0.4:math/py-spreg@${PY_FLAVOR}
+
+USES=		python:3.6+
+USE_PYTHON=	autoplist concurrent distutils
+
+NO_ARCH=	yes
+
+.include <bsd.port.mk>

Added: head/math/py-spglm/distinfo
==============================================================================
--- /dev/null	00:00:00 1970	(empty, because file is newly added)
+++ head/math/py-spglm/distinfo	Sun Jan  3 19:57:28 2021	(r560045)
@@ -0,0 +1,3 @@
+TIMESTAMP = 1609598745
+SHA256 (spglm-1.0.8.tar.gz) = df83b8f7caa41c8aebc4cc39179e40e8670783b06ee567b59bbbe818b773f300
+SIZE (spglm-1.0.8.tar.gz) = 37240

Added: head/math/py-spglm/pkg-descr
==============================================================================
--- /dev/null	00:00:00 1970	(empty, because file is newly added)
+++ head/math/py-spglm/pkg-descr	Sun Jan  3 19:57:28 2021	(r560045)
@@ -0,0 +1,18 @@
+This module is an adaptation of a portion of GLM functionality from the
+Statsmodels package, this it has been simplified and customized for the purposes
+of serving as the base for several other PySAL modules, namely SpInt and GWR.
+Currently, it supports the estimation of Gaussian, Poisson, and Logistic
+regression using only iteratively weighted least squares estimation (IWLS). One
+of the large differences this module and the functions avaialble in the
+Statsmodels package is that the custom IWLS routine is fully sparse compatible,
+which was necesary for the very sparse design matrices that arise in constrained
+spatial interaction models. The somewhat limited functionality and computation
+of only a subset of GLM diagnostics also decreases the computational overhead.
+Another difference is that this module also supports the estimation of
+QuasiPoisson models. One caveat is that this custom IWLS routine currently
+generates estimates by directly solves the least squares normal equations rather
+than using a more robust method like the pseudo inverse. For more robust
+estimation of ill conditioned data and a fuller GLM framework we suggest using
+the original GLM functionality from Statsmodels.
+
+WWW: https://github.com/pysal/spglm