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.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
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:

7.77 score 23 stars 5 packages 343 scripts 761 downloads 1 mentions 5 exports 2 dependencies

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

TargetResultTimeFilesSyslog
linux-devel-arm64OK152
linux-devel-x86_64OK129
source / vignettesOK155
linux-release-arm64OK159
linux-release-x86_64OK125
macos-release-arm64OK191
macos-release-x86_64OK311
macos-oldrel-arm64OK176
macos-oldrel-x86_64OK326
windows-develOK99
windows-releaseOK96
windows-oldrelOK80
wasm-releaseOK94

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