py-spglm Sparse generalize linear models

1.0.8 math =0 Version of this port present on the latest quarterly branch.

Maintainer: sunpoet@FreeBSD.org

Port Added: 2021-01-03 19:58:41

Last Update: 2021-01-03 19:57:28

SVN Revision: 560045

Also Listed In: python

License: BSD3CLAUSE

Description:

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

SVNWeb : Homepage

There is no configure plist information for this port.

• ${PYTHON_PKGNAMEPREFIX}spglm>0:math/py-spglm@${PY_FLAVOR}

To install the port: cd /usr/ports/math/py-spglm/ && make install clean

To add the package: pkg install py37-spglm

PKGNAME: py37-spglm

Package flavors (<flavor>: <package>)

• py37: py37-spglm

distinfo:

TIMESTAMP = 1609598745 SHA256 (spglm-1.0.8.tar.gz) = df83b8f7caa41c8aebc4cc39179e40e8670783b06ee567b59bbbe818b773f300 SIZE (spglm-1.0.8.tar.gz) = 37240

Dependencies

NOTE: FreshPorts displays only information on required and default dependencies. Optional dependencies are not covered.

Build dependencies:

1. py37-setuptools>0 : devel/py-setuptools@py37
2. python3.7 : lang/python37

Runtime dependencies:

1. py37-libpysal>=4.0.0 : science/py-libpysal@py37
2. py37-numpy>=1.3,1 : math/py-numpy@py37
3. py37-scipy>=0.11 : science/py-scipy@py37
4. py37-spreg>=1.0.4 : math/py-spreg@py37
5. py37-setuptools>0 : devel/py-setuptools@py37
6. python3.7 : lang/python37

This port is required by:

for Run

1. graphics/py-mgwr
2. math/py-spint
3. science/py-pysal

Configuration Options

No options to configure

USES:

python:3.6+

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.

• 2017-11-30

  Affects: */py*

  Author: mat@FreeBSD.org

  Reason: 
    Ports using Python via USES=python are now flavored.  All the py3-* ports
    have been removed and folded into their py-* master ports.
  
    People using Poudriere 3.2+ and binary packages do not have to do anything.
  
    For other people, to build the Python 3.6 version of, for example,
    databases/py-gdbm, you need to run:
  
      # make FLAVOR=py36 install
  
  

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

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Last updated:
2021-01-23 18:22:23