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.

5 files changed, 53 insertions, 0 deletions

diff --git a/science/Makefile b/science/Makefile
index 96c2fdb71ae7..0b3aa4567874 100644
--- a/science/Makefile
+++ b/science/Makefile
@@ -349,6 +349,7 @@
     SUBDIR += py-geometric
     SUBDIR += py-gpaw
     SUBDIR += py-gsd
+    SUBDIR += py-gwlearn
     SUBDIR += py-h5json
     SUBDIR += py-h5netcdf
     SUBDIR += py-h5py
diff --git a/science/py-gwlearn/Makefile b/science/py-gwlearn/Makefile
new file mode 100644
index 000000000000..d0fe9cd6ef70
--- /dev/null
+++ b/science/py-gwlearn/Makefile
@@ -0,0 +1,31 @@
+PORTNAME=	gwlearn
+PORTVERSION=	0.1.1
+CATEGORIES=	science python
+MASTER_SITES=	PYPI
+PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER=	sunpoet@FreeBSD.org
+COMMENT=	Geographically weighted modeling based on scikit-learn
+WWW=		https://pysal.org/gwlearn/stable/ \
+		https://github.com/pysal/gwlearn
+
+LICENSE=	BSD3CLAUSE
+LICENSE_FILE=	${WRKSRC}/LICENSE
+
+BUILD_DEPENDS=	${PYTHON_PKGNAMEPREFIX}setuptools>=61.0:devel/py-setuptools@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}setuptools-scm>=6.2:devel/py-setuptools-scm@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR}
+RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}geopandas>=1.0.0:graphics/py-geopandas@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}joblib>=1.4.0:devel/py-joblib@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}libpysal>=4.12:science/py-libpysal@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}numpy1>=1.26.0:math/py-numpy1@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}pandas>=2.1.0,1:math/py-pandas@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}scikit-learn>=1.4.0:science/py-scikit-learn@${PY_FLAVOR} \
+		${PYTHON_PKGNAMEPREFIX}scipy>=1.12.0,1:science/py-scipy@${PY_FLAVOR}
+
+USES=		python
+USE_PYTHON=	autoplist concurrent pep517
+
+NO_ARCH=	yes
+
+.include <bsd.port.mk>
diff --git a/science/py-gwlearn/distinfo b/science/py-gwlearn/distinfo
new file mode 100644
index 000000000000..713c0b8f31bc
--- /dev/null
+++ b/science/py-gwlearn/distinfo
@@ -0,0 +1,3 @@
+TIMESTAMP = 1771510079
+SHA256 (gwlearn-0.1.1.tar.gz) = a125d559f9a38f8285b80dd1c8d89225f9c1bce87874ff25f8312af2e35d0a66
+SIZE (gwlearn-0.1.1.tar.gz) = 2206934
diff --git a/science/py-gwlearn/files/patch-pyproject.toml b/science/py-gwlearn/files/patch-pyproject.toml
new file mode 100644
index 000000000000..599d07641828
--- /dev/null
+++ b/science/py-gwlearn/files/patch-pyproject.toml
@@ -0,0 +1,11 @@
+--- pyproject.toml.orig	2026-01-21 19:55:20 UTC
++++ pyproject.toml
+@@ -11,7 +11,7 @@ authors = [
+ authors = [
+    { name = "Martin Fleischmann", email = "martin@martinfleischmann.net" },
+ ]
+-license = "BSD-3-Clause"
++license = { text = "BSD-3-Clause" }
+ classifiers = [
+     "Programming Language :: Python :: 3",
+     "Operating System :: OS Independent",
diff --git a/science/py-gwlearn/pkg-descr b/science/py-gwlearn/pkg-descr
new file mode 100644
index 000000000000..61d4547961c8
--- /dev/null
+++ b/science/py-gwlearn/pkg-descr
@@ -0,0 +1,7 @@
+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.