mllrnrs: R6-Based ML Learners for 'mlexperiments'

Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners. The package provides R6-based learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'lightgbm' <https://CRAN.R-project.org/package=lightgbm>. These can be used directly with the 'mlexperiments' R package.

Version: 0.0.2
Depends: R (≥ 2.10)
Imports: data.table, kdry, mlexperiments, R6, stats
Suggests: glmnet, knitr, lightgbm, lintr, mlbench, mlr3measures, ParBayesianOptimization, ranger, splitTools, testthat (≥ 3.0.1), xgboost
Published: 2023-07-18
Author: Lorenz A. Kapsner ORCID iD [cre, aut, cph]
Maintainer: Lorenz A. Kapsner <lorenz.kapsner at gmail.com>
BugReports: https://github.com/kapsner/mllrnrs/issues
License: GPL (≥ 3)
URL: https://github.com/kapsner/mllrnrs
NeedsCompilation: no
CRAN checks: mllrnrs results

Documentation:

Reference manual: mllrnrs.pdf
Vignettes: glmnet: Binary Classification
glmnet: Multiclass Classification
glmnet: Regression
lightgbm: Binary Classification
lightgbm: Multiclass Classification
lightgbm: Regression
ranger: Binary Classification
ranger: Multiclass Classification
ranger: Regression
xgboost: Binary Classification
xgboost: Multiclass Classification
xgboost: Regression

Downloads:

Package source: mllrnrs_0.0.2.tar.gz
Windows binaries: r-devel: mllrnrs_0.0.2.zip, r-release: mllrnrs_0.0.2.zip, r-oldrel: mllrnrs_0.0.2.zip
macOS binaries: r-release (arm64): mllrnrs_0.0.2.tgz, r-oldrel (arm64): mllrnrs_0.0.2.tgz, r-release (x86_64): mllrnrs_0.0.2.tgz, r-oldrel (x86_64): mllrnrs_0.0.2.tgz

Reverse dependencies:

Reverse imports: mlsurvlrnrs

Linking:

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