Package: gamlr 1.13-9

Matt Taddy

gamlr: Gamma Lasso Regression

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2017 JCGS), 'One-Step Estimator Paths for Concave Regularization', <doi:10.48550/arXiv.1308.5623>.

Authors:Matt Taddy [aut, cre]

gamlr_1.13-9.tar.gz
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gamlr_1.13-9.tar.gz(r-4.7-arm64)gamlr_1.13-9.tar.gz(r-4.7-x86_64)gamlr_1.13-9.tar.gz(r-4.6-arm64)gamlr_1.13-9.tar.gz(r-4.6-x86_64)
gamlr_1.13-9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
gamlr/json (API)

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

Bug tracker:https://github.com/taddylab/gamlr/issues

Datasets:

On CRAN:

Conda:

7.91 score 23 stars 7 packages 311 scripts 1.1k downloads 1 mentions 5 exports 2 dependencies

Last updated from:b8cf4d07ee. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK130
linux-devel-x86_64OK110
source / vignettesOK158
linux-release-arm64OK137
linux-release-x86_64OK158
macos-release-arm64OK100
macos-release-x86_64OK230
macos-oldrel-arm64OK96
macos-oldrel-x86_64OK181
windows-develOK103
windows-releaseOK90
windows-oldrelOK103
wasm-releaseOK92

Exports:AICccv.gamlrdoubleMLgamlrnaref

Dependencies:latticeMatrix

Readme and manuals

Help Manual

Help pageTopics
Corrected AICAICc
Cross Validation for gamlrcoef.cv.gamlr cv.gamlr plot.cv.gamlr predict.cv.gamlr
double MLdoubleML
Gamma-Lasso regressioncoef.gamlr gamlr logLik.gamlr plot.gamlr predict.gamlr
NHL hockey dataconfig goal hockey player team
NA reference levelnaref