diff --git a/math/py-statsmodels/Makefile b/math/py-statsmodels/Makefile
index 669980fc9f70..d6cbc65701a2 100644
--- a/math/py-statsmodels/Makefile
+++ b/math/py-statsmodels/Makefile
@@ -1,43 +1,44 @@
 PORTNAME=	statsmodels
 PORTVERSION=	0.14.1
 CATEGORIES=	math python
 MASTER_SITES=	PYPI
 PKGNAMEPREFIX=	${PYTHON_PKGNAMEPREFIX}
 
 MAINTAINER=	sunpoet@FreeBSD.org
 COMMENT=	Complement to SciPy for statistical computations
-WWW=		https://github.com/statsmodels/statsmodels
+WWW=		https://www.statsmodels.org/stable/ \
+		https://github.com/statsmodels/statsmodels
 
 LICENSE=	BSD3CLAUSE
 LICENSE_FILE=	${WRKSRC}/LICENSE.txt
 
 BUILD_DEPENDS=	${PYTHON_PKGNAMEPREFIX}numpy>=1.22.3,1<2,1:math/py-numpy@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}scipy>=1.4:science/py-scipy@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}setuptools>=63.1.0:devel/py-setuptools@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}setuptools-scm>=8<9:devel/py-setuptools-scm@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}wheel>=0:devel/py-wheel@${PY_FLAVOR}
 RUN_DEPENDS=	${PYTHON_PKGNAMEPREFIX}numpy>=1.22.3,1<2,1:math/py-numpy@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}packaging>=21.3:devel/py-packaging@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}pandas>=0.25,1:math/py-pandas@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}patsy>=0.5.4:math/py-patsy@${PY_FLAVOR} \
 		${PYTHON_PKGNAMEPREFIX}scipy>=1.4:science/py-scipy@${PY_FLAVOR}
 
 USES=		python shebangfix
 USE_PYTHON=	autoplist concurrent cython pep517
 
 SHEBANG_GLOB=	*.py
 
 OPTIONS_DEFINE=	DOCS EXAMPLES
 
 post-install:
 	${FIND} ${STAGEDIR}${PYTHON_SITELIBDIR} -name '*.so' -exec ${STRIP_CMD} {} +
 
 post-install-DOCS-on:
 	${MKDIR} ${STAGEDIR}${DOCSDIR}
 	${INSTALL_DATA} ${WRKSRC}/README.rst ${WRKSRC}/README_l1.txt ${STAGEDIR}${DOCSDIR}
 
 post-install-EXAMPLES-on:
 	${MKDIR} ${STAGEDIR}${EXAMPLESDIR}
 	cd ${WRKSRC}/examples && ${COPYTREE_SHARE} . ${STAGEDIR}${EXAMPLESDIR}
 
 .include <bsd.port.mk>
diff --git a/math/py-statsmodels/pkg-descr b/math/py-statsmodels/pkg-descr
index 5e5e83d80f62..a87433b76fc1 100644
--- a/math/py-statsmodels/pkg-descr
+++ b/math/py-statsmodels/pkg-descr
@@ -1,22 +1,29 @@
-Statsmodels is a Python package that provides a complement to scipy for
+statsmodels is a Python package that provides a complement to scipy for
 statistical computations including descriptive statistics and estimation and
 inference for statistical models.
 
 Main Features:
-* linear regression models: GLS (including WLS and LS aith AR errors) and OLS.
-* glm: Generalized linear models with support for all of the one-parameter
-  exponential family distributions.
-* discrete: regression with discrete dependent variables, including Logit,
-  Probit, MNLogit, Poisson, based on maximum likelihood estimators
-* rlm: Robust linear models with support for several M-estimators.
-* tsa: models for time series analysis - univariate: AR, ARIMA; multivariate:
-  VAR and structural VAR
-* nonparametric: (Univariate) kernel density estimators
-* datasets: Datasets to be distributed and used for examples and in testing.
-* stats: a wide range of statistical tests, diagnostics and specification tests
-* iolib: Tools for reading Stata .dta files into numpy arrays, printing table
-  output to ascii, latex, and html
-* miscellaneous models
-* sandbox: statsmodels contains a sandbox folder with code in various stages of
-* developement and testing which is not considered "production ready", including
-  Mixed models, GARCH and GMM estimators, kernel regression, panel data models.
+- Linear regression models
+- Mixed Linear Model with mixed effects and variance components
+- GLM: Generalized linear models with support for all of the one-parameter
+  exponential family distributions
+- Bayesian Mixed GLM for Binomial and Poisson
+- GEE: Generalized Estimating Equations for one-way clustered or longitudinal
+  data
+- Discrete models
+- RLM: Robust linear models with support for several M-estimators.
+- Time Series Analysis: models for time series analysis
+- Survival analysis
+- Multivariate
+- Nonparametric statistics: Univariate and multivariate kernel density
+  estimators
+- Datasets: Datasets used for examples and in testing
+- Statistics: a wide range of statistical tests
+- Imputation with MICE, regression on order statistic and Gaussian imputation
+- Mediation analysis
+- Graphics includes plot functions for visual analysis of data and model results
+- I/O
+- Miscellaneous models
+- Sandbox: statsmodels contains a sandbox folder with code in various stages of
+  development and testing which is not considered "production ready". This
+  covers among others