Introduction | Building your first GELnet model | Other regression problems
This package is considered a duplicate. The official version of this package is found at:https://artemsokolov.r-universe.dev/gelnet
Package: gelnet 1.2.2
Artem Sokolov
gelnet: Generalized Elastic Nets
Implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.
Authors:
gelnet_1.2.2.tar.gz
gelnet_1.2.2.tar.gz(r-4.7-arm64)gelnet_1.2.2.tar.gz(r-4.7-x86_64)gelnet_1.2.2.tar.gz(r-4.6-arm64)gelnet_1.2.2.tar.gz(r-4.6-x86_64)
gelnet_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
gelnet/json (API)
| # Install 'gelnet' in R: |
| install.packages('gelnet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:d5e33f9ef0. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 93 | ||
| linux-devel-x86_64 | OK | 162 | ||
| source / vignettes | OK | 158 | ||
| linux-release-arm64 | OK | 144 | ||
| linux-release-x86_64 | OK | 118 | ||
| wasm-release | OK | 124 |
Exports:adj2lapladj2nlaplgelnetgelnet.cvgelnet.kergelnet.lin.objgelnet.logreg.objgelnet.oneclass.objL1.ceiling
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Generate a graph Laplacian | adj2lapl |
| Generate a normalized graph Laplacian | adj2nlapl |
| GELnet for linear regression, binary classification and one-class problems. | gelnet |
| k-fold cross-validation for parameter tuning. | gelnet.cv |
| Kernel models for linear regression, binary classification and one-class problems. | gelnet.ker |
| Linear regression objective function value | gelnet.lin.obj |
| Logistic regression objective function value | gelnet.logreg.obj |
| One-class regression objective function value | gelnet.oneclass.obj |
| The largest meaningful value of the L1 parameter | L1.ceiling |
