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science/py-gwlearn 0.2.0

Geographically weighted modeling based on scikit-learn

0
unaddressed
0
VuXML only
0
fixed
0
open problem reports
Maintainer
sunpoet@FreeBSD.org
Packages
latest: 0.2.0
Upstream
up to date
Last update
to 0.2.0 · 58edb81633
Quarterly
2026Q4: 0.2.0
WWW
https://pysal.org/gwlearn/stable/
Links
cgit · VuXML

Unaddressed and VuXML only (0)

No unaddressed CVE and nothing reported by pkg audit for the packaged version 0.2.0.

Fixed (0)

No VuXML entries for this package.

Problem reports (0) · open in Bugzilla, most recently changed first

None.

Commits (5)

Po-Chuan Hsieh (sunpoet) · 0.2.0
science/py-gwlearn: Update to 0.2.0

Changes:	https://github.com/pysal/gwlearn/releases
58edb81633
Po-Chuan Hsieh (sunpoet) · 0.1.1_1
science/py-gwlearn: Update USES=python
b812d1f95d
Max Brazhnikov (makc) · 0.1.1_1
*/*: bump PORTREVISION after switching to NumPy 2.x
2bf3834a19
Max Brazhnikov (makc) · 0.1.1
*/*: Switch to NumPy 2.x

PR:		294328
Exp-run:	antoine
b0b4d9e1ea
Po-Chuan Hsieh (sunpoet) · 0.1.1
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.
30b7cf8a57