MachineShop: Machine Learning Models and Tools

Meta-package for statistical and machine learning with a common interface for model fitting, prediction, performance assessment, and presentation of results. Supports predictive modeling of numerical, categorical, and censored time-to-event outcomes and resample (bootstrap and cross-validation) estimation of model performance.

Version: 1.3.0
Imports: abind, foreach, ggplot2, kernlab, magrittr, methods, party, polspline, recipes (≥ 0.1.4), rsample, Rsolnp, survival, tibble, utils
Suggests: adabag, BART, bartMachine, C50, doParallel, e1071, earth, gbm, glmnet, Hmisc, kableExtra, kknn, knitr, lars, mda, MASS, mboost, nnet, partykit, pls, randomForest, ranger, rmarkdown, rms, rpart, testthat, tree, xgboost
Published: 2019-04-23
Author: Brian J Smith [aut, cre]
Maintainer: Brian J Smith <brian-j-smith at uiowa.edu>
BugReports: https://github.com/brian-j-smith/MachineShop/issues
License: GPL-3
URL: https://brian-j-smith.github.io/MachineShop/
NeedsCompilation: no
Citation: MachineShop citation info
Materials: README NEWS
CRAN checks: MachineShop results

Downloads:

Reference manual: MachineShop.pdf
Vignettes: Introduction to the MachineShop Package
Conventions for MLModels Implementation
Package source: MachineShop_1.3.0.tar.gz
Windows binaries: r-devel: MachineShop_1.3.0.zip, r-release: MachineShop_1.3.0.zip, r-oldrel: MachineShop_1.3.0.zip
OS X binaries: r-release: MachineShop_1.3.0.tgz, r-oldrel: MachineShop_1.2.0.tgz
Old sources: MachineShop archive

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