| Port details |
- py-spglm Sparse generalize linear models
- 1.0.8 math
 =0      1.0.8Version of this port present on the latest quarterly branch. - Maintainer: sunpoet@FreeBSD.org
- 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:
- 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 : 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>)
- distinfo:
- TIMESTAMP = 1609598745
SHA256 (spglm-1.0.8.tar.gz) = df83b8f7caa41c8aebc4cc39179e40e8670783b06ee567b59bbbe818b773f300
SIZE (spglm-1.0.8.tar.gz) = 37240
- Packages (timestamps in pop-ups are UTC):
- Dependencies
- NOTE: FreshPorts displays only information on required and default dependencies. Optional dependencies are not covered.
- Build dependencies:
-
- py38-setuptools>0 : devel/py-setuptools@py38
- python3.8 : lang/python38
- Runtime dependencies:
-
- py38-libpysal>=4.0.0 : science/py-libpysal@py38
- py38-numpy>=1.3,1 : math/py-numpy@py38
- py38-scipy>=0.11 : science/py-scipy@py38
- py38-spreg>=1.0.4 : math/py-spreg@py38
- py38-setuptools>0 : devel/py-setuptools@py38
- python3.8 : lang/python38
- This port is required by:
- for Run
-
- graphics/py-mgwr
- math/py-spint
- science/py-pysal
- Configuration Options:
- No options to configure
- Options name:
- math_py-spglm
- USES:
- python:3.7+
- FreshPorts was unable to extract/find any pkg message
- Master Sites:
|
Number of commits found: 3
| Commit History - (may be incomplete: see SVNWeb link above for full details) |
| Date | By | Description |
|---|
06 Apr 2021 14:31:07
  1.0.8
|
Mathieu Arnold (mat) |
Remove # $FreeBSD$ from Makefiles. |
30 Jan 2021 13:29:58
  1.0.8
|
sunpoet |
Update USES=python: math/py-pandas 1.2.0 requires Python 3.7+
PR: 250103 |
03 Jan 2021 19:57:28
1.0.8 
|
sunpoet |
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
|
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