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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:	0.0.4
Depends:	R (≥ 3.6)
Imports:	data.table, kdry, mlexperiments, R6, stats
Suggests:	glmnet, lightgbm (≥ 4.0.0), lintr, mlbench, mlr3measures, ParBayesianOptimization, quarto, ranger, splitTools, testthat (≥ 3.0.1), xgboost
Published:	2024-07-05
DOI:	10.32614/CRAN.package.mllrnrs
Author:	Lorenz A. Kapsner [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
SystemRequirements:	Quarto command line tools (https://github.com/quarto-dev/quarto-cli).
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_0.0.4.tar.gz
Windows binaries:	r-devel: mllrnrs_0.0.4.zip, r-release: mllrnrs_0.0.4.zip, r-oldrel: mllrnrs_0.0.4.zip
macOS binaries:	r-release (arm64): mllrnrs_0.0.4.tgz, r-oldrel (arm64): mllrnrs_0.0.4.tgz, r-release (x86_64): mllrnrs_0.0.4.tgz, r-oldrel (x86_64): mllrnrs_0.0.4.tgz
Old sources:	mllrnrs archive

Reverse dependencies:

Reverse imports:

mlsurvlrnrs

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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