fastml: Fast Machine Learning Model Training and Evaluation

Streamlines the training, evaluation, and comparison of multiple machine learning models with minimal code by providing comprehensive data preprocessing and support for a wide range of algorithms with hyperparameter tuning. It offers performance metrics and visualization tools to facilitate efficient and effective machine learning workflows.

Version: 0.7.0
Imports: methods, recipes, dplyr, ggplot2, reshape2, rsample, parsnip, tune, workflows, yardstick, tibble, rlang, dials, RColorBrewer, baguette, bonsai, discrim, doFuture, finetune, future, plsmod, probably, viridisLite, DALEX, magrittr, pROC, janitor, stringr, DT, UpSetR, VIM, broom, dbscan, ggpubr, gridExtra, htmlwidgets, kableExtra, moments, naniar, plotly, scales, skimr, tidyr, tidyselect, purrr, mice, missForest, survival, flexsurv, rstpm2, iml, lime, survRM2, ceterisParibus, xgboost, knitr, rmarkdown
Suggests: testthat (≥ 3.0.0), C50, ranger, aorsf, censored, crayon, kernlab, klaR, kknn, keras, lightgbm, rstanarm, mixOmics, pdp, patchwork, GGally, glmnet, agua, bslib, h2o, mlbench, tidyverse
Published: 2025-10-29
DOI: 10.32614/CRAN.package.fastml
Author: Selcuk Korkmaz ORCID iD [aut, cre], Dincer Goksuluk ORCID iD [aut], Eda Karaismailoglu ORCID iD [aut]
Maintainer: Selcuk Korkmaz <selcukorkmaz at gmail.com>
BugReports: https://github.com/selcukorkmaz/fastml/issues
License: MIT + file LICENSE
URL: https://selcukorkmaz.github.io/fastml-tutorial/, https://github.com/selcukorkmaz/fastml
NeedsCompilation: no
Materials: README
CRAN checks: fastml results

Documentation:

Reference manual: fastml.html , fastml.pdf

Downloads:

Package source: fastml_0.7.0.tar.gz
Windows binaries: r-devel: fastml_0.6.2.zip, r-release: fastml_0.6.2.zip, r-oldrel: fastml_0.6.2.zip
macOS binaries: r-release (arm64): fastml_0.7.0.tgz, r-oldrel (arm64): fastml_0.7.0.tgz, r-release (x86_64): fastml_0.7.0.tgz, r-oldrel (x86_64): fastml_0.7.0.tgz
Old sources: fastml archive

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