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Port details
py-spglm Sparse generalize linear models
1.0.8 math on this many watch lists=0 search for ports that depend on this port Find issues related to this port Report an issue related to this port View this port on Repology. pkg-fallout 1.0.8Version of this port present on the latest quarterly branch.
Maintainer: sunpoet@FreeBSD.org search for ports maintained by this maintainer
Port Added: 2021-01-03 19:58:41
Last Update: 2021-04-06 14:31:07
Commit Hash: 305f148
Also Listed In: python
License: BSD3CLAUSE
Description:
SVNWeb : git : Homepage
pkg-plist: as obtained via: make generate-plist
There is no configure plist information for this port.
Dependency lines:
  • ${PYTHON_PKGNAMEPREFIX}spglm>0:math/py-spglm@${PY_FLAVOR}
To install the port: cd /usr/ports/math/py-spglm/ && make install clean
To add the package, run one of these commands:
  • pkg install math/py-spglm
  • pkg install py38-spglm
NOTE: This is a Python port. Instead of py38-spglm listed in the above command, you can pick from the names under the Packages section.
PKGNAME: py38-spglm
Package flavors (<flavor>: <package>)
  • py38: py38-spglm
distinfo:
Packages (timestamps in pop-ups are UTC):
py37-spglm
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py38-spglm
ABIlatestquarterly
FreeBSD:11:aarch64--
FreeBSD:11:amd641.0.81.0.8
FreeBSD:11:armv6--
FreeBSD:11:i3861.0.81.0.8
FreeBSD:11:mips--
FreeBSD:11:mips64--
FreeBSD:12:aarch64-1.0.8
FreeBSD:12:amd641.0.81.0.8
FreeBSD:12:armv6--
FreeBSD:12:armv7--
FreeBSD:12:i3861.0.81.0.8
FreeBSD:12:mips--
FreeBSD:12:mips64--
FreeBSD:12:powerpc64--
FreeBSD:13:aarch64-1.0.8
FreeBSD:13:amd641.0.81.0.8
FreeBSD:13:armv6--
FreeBSD:13:armv7--
FreeBSD:13:i3861.0.81.0.8
FreeBSD:13:mips--
FreeBSD:13:mips64--
FreeBSD:13:powerpc64--
FreeBSD:14:aarch641.0.8-
FreeBSD:14:amd641.0.8-
FreeBSD:14:armv6--
FreeBSD:14:armv7--
FreeBSD:14:i3861.0.8-
FreeBSD:14:mips--
FreeBSD:14:mips64--
FreeBSD:14:powerpc64--
 

Dependencies
NOTE: FreshPorts displays only information on required and default dependencies. Optional dependencies are not covered.
Build dependencies:
  1. py38-setuptools>0 : devel/py-setuptools@py38
  2. python3.8 : lang/python38
Runtime dependencies:
  1. py38-libpysal>=4.0.0 : science/py-libpysal@py38
  2. py38-numpy>=1.3,1 : math/py-numpy@py38
  3. py38-scipy>=0.11 : science/py-scipy@py38
  4. py38-spreg>=1.0.4 : math/py-spreg@py38
  5. py38-setuptools>0 : devel/py-setuptools@py38
  6. python3.8 : lang/python38
This port is required by:
for Run
  1. graphics/py-mgwr
  2. math/py-spint
  3. science/py-pysal

Configuration Options:
Options name:

USES:

FreshPorts was unable to extract/find any pkg message
Master Sites:
Expand this list (2 items)
Collapse this list.
  1. https://files.pythonhosted.org/packages/source/s/spglm/
  2. https://pypi.org/packages/source/s/spglm/
Collapse this list.

Number of commits found: 3

Commit History - (may be incomplete: see SVNWeb link above for full details)
DateByDescription
06 Apr 2021 14:31:07
 files touched by this commit commit hash:305f148f482daf30dcf728039d03d019f88344eb  1.0.8
mat search for other commits by this committer
Remove # $FreeBSD$ from Makefiles.
30 Jan 2021 13:29:58
Original commit files touched by this commit Revision:563407  1.0.8
sunpoet search for other commits by this committer
Update USES=python: math/py-pandas 1.2.0 requires Python 3.7+

PR:		250103
03 Jan 2021 19:57:28
Original commit files touched by this commit Revision:560045  1.0.8  Sanity Test Failure
sunpoet search for other commits by this committer
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

Number of commits found: 3