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:
gamlr_1.13-9.tar.gz
gamlr_1.13-9.zip(r-4.7-x86_64)gamlr_1.13-9.zip(r-4.7-arm64)gamlr_1.13-9.zip(r-4.6-x86_64)gamlr_1.13-9.zip(r-4.6-arm64)gamlr_1.13-9.zip(r-4.5-x86_64)
gamlr_1.13-9.tgz(r-4.6-x86_64)gamlr_1.13-9.tgz(r-4.6-arm64)gamlr_1.13-9.tgz(r-4.5-x86_64)gamlr_1.13-9.tgz(r-4.5-arm64)
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✨
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
Last updated from:b8cf4d07ee. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 111 | ||
| linux-devel-x86_64 | OK | 108 | ||
| source / vignettes | OK | 144 | ||
| linux-release-arm64 | OK | 105 | ||
| linux-release-x86_64 | OK | 116 | ||
| macos-release-arm64 | OK | 148 | ||
| macos-release-x86_64 | OK | 258 | ||
| macos-oldrel-arm64 | OK | 176 | ||
| macos-oldrel-x86_64 | OK | 348 | ||
| windows-devel-arm64 | OK | 332 | ||
| windows-devel-x86_64 | OK | 134 | ||
| windows-release-arm64 | OK | 329 | ||
| windows-release-x86_64 | OK | 86 | ||
| windows-oldrel-x86_64 | OK | 93 | ||
| wasm-release | OK | 80 |
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Corrected AIC | AICc |
| Cross Validation for gamlr | coef.cv.gamlr cv.gamlr plot.cv.gamlr predict.cv.gamlr |
| double ML | doubleML |
| Gamma-Lasso regression | coef.gamlr gamlr logLik.gamlr plot.gamlr predict.gamlr |
| NHL hockey data | config goal hockey player team |
| NA reference level | naref |