# Buildsheet autogenerated by ravenadm tool -- Do not edit.

NAMEBASE=		R-glmnet
VERSION=		4.1-10
KEYWORDS=		cran
VARIANTS=		std
SDESC[std]=		Generalized Linear Models for Lasso, etc
HOMEPAGE=		https://glmnet.stanford.edu
CONTACT=		CRAN_Automaton[cran@ironwolf.systems]

DOWNLOAD_GROUPS=	main
SITES[main]=		CRAN/src/contrib
			https://loki.dragonflybsd.org/cranfiles/
DISTFILE[1]=		glmnet_4.1-10.tar.gz:main
DIST_SUBDIR=		CRAN
DF_INDEX=		1
SPKGS[std]=		single

OPTIONS_AVAILABLE=	none
OPTIONS_STANDARD=	none

BUILDRUN_DEPENDS=	R-foreach:single:std
			R-shape:single:std
			R-Rcpp:single:std
			R-RcppEigen:single:std

USES=			cran gmake gettext:build

DISTNAME=		glmnet

GENERATED=		yes

INSTALL_REQ_TOOLCHAIN=	yes

[FILE:1104:descriptions/desc.single]
glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

Extremely efficient procedures for fitting the entire lasso or elastic-net
regularization path for linear regression, logistic and multinomial
regression models, Poisson regression, Cox model, multiple-response
Gaussian, and the grouped multinomial regression; see &lt;<a
href="https://doi.org/10.18637%2Fjss.v033.i01"
target="_top">doi:10.18637/jss.v033.i01</a>&gt; and &lt;<a
href="https://doi.org/10.18637%2Fjss.v039.i05"
target="_top">doi:10.18637/jss.v039.i05</a>&gt;. There are two new and
important additions. The family argument can be a GLM family object, which
opens the door to any programmed family (&lt;<a
href="https://doi.org/10.18637%2Fjss.v106.i01"
target="_top">doi:10.18637/jss.v106.i01</a>&gt;). This comes with a modest
computational cost, so when the built-in families suffice, they should be
used instead. The other novelty is the relax option, which refits each of
the active sets in the path unpenalized. The algorithm uses cyclical
coordinate descent in a path-wise fashion, as described in the papers
cited.


[FILE:104:distinfo]
89a4b5844850b27e87e879a19bd20ed7f1d9f555adc94a8e88935c52677f2e9c      2419157 CRAN/glmnet_4.1-10.tar.gz