| Port details |
- py-gwlearn Geographically weighted modeling based on scikit-learn
- 0.2.0 science
 =0      Package not present on quarterly.This port was created during this quarter. It will be in the next quarterly branch but not the current one. - Maintainer: sunpoet@FreeBSD.org
- Port Added: 2026-05-05 14:00:06
- Last Update: 2026-06-26 20:45:15
- Commit Hash: 58edb81
- Also Listed In: python
- License: BSD3CLAUSE
- WWW:
- https://pysal.org/gwlearn/stable/
- https://github.com/pysal/gwlearn
- Description:
- The aim of the package is to provide implementations of spatially-explicit
modelling.
gwlearn provides a framework for prototyping geographically weighted extensions
of regression and classification models based on scikit-learn and libpysal.graph
and a subset of models implemented on top of this framework. For example, you
can run geographically weighted linear regression in a following manner.
  ¦  ¦  ¦  ¦ 
- Manual pages:
- FreshPorts has no man page information for this port.
- pkg-plist: as obtained via:
make generate-plist - There is no configure plist information for this port.
- USE_RC_SUBR (Service Scripts)
- no SUBR information found for this port
- Dependency lines:
-
- ${PYTHON_PKGNAMEPREFIX}gwlearn>0:science/py-gwlearn@${PY_FLAVOR}
- To install the port:
- cd /usr/ports/science/py-gwlearn/ && make install clean
- To add the package, run one of these commands:
- pkg install science/py-gwlearn
- pkg install py312-gwlearn
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above.
NOTE: This is a Python port. Instead of py312-gwlearn listed in the above command, you can pick from the names under the Packages section.- PKGNAME: py312-gwlearn
- Package flavors (<flavor>: <package>)
- distinfo:
- TIMESTAMP = 1780751968
SHA256 (gwlearn-0.2.0.tar.gz) = 29d24341f71e9851dba0cf99fd91395f788ffb8203b12d56db7c0f9d307df779
SIZE (gwlearn-0.2.0.tar.gz) = 2216493
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:
-
- py312-setuptools>=61.0 : devel/py-setuptools@py312
- py312-setuptools-scm>=6.2 : devel/py-setuptools-scm@py312
- py312-wheel>=0 : devel/py-wheel@py312
- python3.12 : lang/python312
- py312-build>=0 : devel/py-build@py312
- py312-installer>=0 : devel/py-installer@py312
- Test dependencies:
-
- python3.12 : lang/python312
- Runtime dependencies:
-
- py312-geopandas>=1.1.0 : graphics/py-geopandas@py312
- py312-joblib>=1.5.0 : devel/py-joblib@py312
- py312-libpysal>=4.12 : science/py-libpysal@py312
- py312-numpy>=2.0.0,1 : math/py-numpy@py312
- py312-pandas>=2.3.0,1 : math/py-pandas@py312
- py312-scikit-learn>=1.5.0 : science/py-scikit-learn@py312
- py312-scipy>=1.14.0,1 : science/py-scipy@py312
- python3.12 : lang/python312
- This port is required by:
- for Run
-
- science/py-pysal
Configuration Options:- No options to configure
- Options name:
- science_py-gwlearn
- USES:
- python:3.12+
- FreshPorts was unable to extract/find any pkg message
- Master Sites:
|
Number of commits found: 5
| Commit History - (may be incomplete: for full details, see links to repositories near top of page) |
| Commit | Credits | Log message |
|---|
0.2.0
26 Jun 2026 20:45:15
 |
Po-Chuan Hsieh (sunpoet) |
science/py-gwlearn: Update to 0.2.0
Changes: https://github.com/pysal/gwlearn/releases |
0.1.1_1
09 May 2026 22:57:53
|
Po-Chuan Hsieh (sunpoet) |
science/py-gwlearn: Update USES=python |
0.1.1_1
05 May 2026 19:29:37
|
Max Brazhnikov (makc) |
*/*: bump PORTREVISION after switching to NumPy 2.x |
0.1.1
05 May 2026 19:29:37
 |
Max Brazhnikov (makc) |
*/*: Switch to NumPy 2.x
PR: 294328
Exp-run: antoine |
0.1.1
05 May 2026 13:56:14
|
Po-Chuan Hsieh (sunpoet) |
science/py-gwlearn: Add py-gwlearn 0.1.1
The aim of the package is to provide implementations of spatially-explicit
modelling.
gwlearn provides a framework for prototyping geographically weighted extensions
of regression and classification models based on scikit-learn and libpysal.graph
and a subset of models implemented on top of this framework. For example, you
can run geographically weighted linear regression in a following manner. |
Number of commits found: 5
|
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