Package: sparsenet 1.7

Trevor Hastie

sparsenet: Fit Sparse Linear Regression Models via Nonconvex Optimization

Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <doi:10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.

Authors:Trevor Hastie [aut, cre], Rahul Mazumder [aut], Jerome Friedman [aut]

sparsenet_1.7.tar.gz
sparsenet_1.7.tar.gz(r-4.5-noble)sparsenet_1.7.tar.gz(r-4.4-noble)
sparsenet_1.7.tgz(r-4.4-emscripten)sparsenet_1.7.tgz(r-4.3-emscripten)
sparsenet.pdf |sparsenet.html
sparsenet/json (API)

# Install 'sparsenet' in R:
install.packages('sparsenet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • fortran– Runtime library for GNU Fortran applications

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortran

2.38 score 2 stars 1 packages 20 scripts 330 downloads 1 mentions 14 exports 3 dependencies

Last updated 19 days agofrom:ea4cba4da1. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 25 2024
R-4.5-linux-x86_64OKNov 25 2024

Exports:coef.cv.sparsenetcoef.sparsenetcv.sparsenetgendatagetcoef_listplot.cv.sparsenetplot.sparsenetpredict.cv.sparsenetpredict.sparsenetprint.cv.sparsenetprint.sparsenetsparsenetsparsepredictsummary.sparsenet

Dependencies:latticeMatrixshape