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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 [aut, cre], Dincer Goksuluk [aut], Eda Karaismailoglu [aut]
Maintainer:	Selcuk Korkmaz <selcukorkmaz at gmail.com>
BugReports:	https://github.com/selcukorkmaz/fastml/issues
License:	MIT + fileLICENSE
URL:	https://selcukorkmaz.github.io/fastml-tutorial/,https://github.com/selcukorkmaz/fastml
NeedsCompilation:	no
Materials:	README
CRAN checks:	fastml results

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