abind, cli, dials (>= 0.0.4), foreach, ggplot2 (>= 3.3.0),
kernlab, magrittr, Matrix, methods, nnet, party, polspline,
progress, recipes (>= 0.1.4), rlang, rsample (>= 0.1.0),
Rsolnp, survival, tibble, utils
adabag, BART, bartMachine, C50, cluster, doParallel, e1071,
earth, elasticnet, gbm, glmnet, gridExtra, Hmisc, kableExtra,
kknn, knitr, lars, MASS, mboost, mda, parsnip, partykit, pls,
randomForest, randomForestSRC, ranger, rmarkdown, rms, rpart,
testthat, tree, xgboost
'classes.R' 'MLControl.R' 'MLMetric.R' 'MLModel.R'
'ML_AdaBagModel.R' 'ML_AdaBoostModel.R' 'ML_BARTMachineModel.R'
'ML_BARTModel.R' 'ML_BlackBoostModel.R' 'ML_C50Model.R'
'ML_CForestModel.R' 'ML_CoxModel.R' 'ML_EarthModel.R'
'ML_FDAModel.R' 'ML_GAMBoostModel.R' 'ML_GBMModel.R'
'ML_GLMBoostModel.R' 'ML_GLMModel.R' 'ML_GLMNetModel.R'
'ML_KNNModel.R' 'ML_LARSModel.R' 'ML_LDAModel.R' 'ML_LMModel.R'
'ML_MDAModel.R' 'ML_NNetModel.R' 'ML_NaiveBayesModel.R'
'ML_Null.R' 'ML_ParsnipModel.R' 'ML_PLSModel.R'
'ML_POLRModel.R' 'ML_QDAModel.R' 'ML_RFSRCModel.R'
'ML_RPartModel.R' 'ML_RandomForestModel.R' 'ML_RangerModel.R'
'ML_SVMModel.R' 'ML_StackedModel.R' 'ML_SuperModel.R'
'ML_SurvRegModel.R' 'ML_TreeModel.R' 'ML_XGBModel.R'
'MachineShop-package.R' 'ModelFrame.R' 'ModelRecipe.R'
'ModeledInput.R' 'TrainedInputs.R' 'TrainedModels.R' 'append.R'
'calibration.R' 'case_comps.R' 'coerce.R' 'combine.R'
'conditions.R' 'confusion.R' 'convert.R' 'data.R'
'dependence.R' 'diff.R' 'expand.R' 'extract.R' 'fit.R' 'grid.R'
'metricinfo.R' 'metrics.R' 'metrics_factor.R'
'metrics_numeric.R' 'modelinfo.R' 'models.R' 'performance.R'
'performance_curve.R' 'plot.R' 'predict.R' 'predictors.R'
'print.R' 'recipe_roles.R' 'reexports.R' 'resample.R'
'response.R' 'rfe.R' 'settings.R' 'step_kmeans.R'
'step_kmedoids.R' 'step_lincomp.R' 'step_sbf.R' 'step_spca.R'
'summary.R' 'survival.R' 'utils.R' 'varimp.R'

abind, cli (>= 3.1.0), dials (>= 0.0.4), foreach, ggplot2 (>=
3.3.0), kernlab, magrittr, Matrix, methods, nnet, party,
polspline, progress, recipes (>= 0.1.4), rlang, rsample (>=
0.1.0), Rsolnp, survival, tibble, utils
adabag, BART, bartMachine, C50, cluster, doParallel, e1071,
earth, elasticnet, generics, gbm, glmnet, gridExtra, Hmisc,
kableExtra, kknn, knitr, lars, MASS, mboost, mda,
ParBayesianOptimization, parsnip, partykit, pls, pso,
randomForest, randomForestSRC, ranger, rBayesianOptimization,
rmarkdown, rms, rpart, testthat, tree, xgboost
'classes.R' 'MLControl.R' 'MLInput.R' 'MLMetric.R' 'MLModel.R'
'MLOptimization.R' 'ML_AdaBagModel.R' 'ML_AdaBoostModel.R'
'ML_BARTMachineModel.R' 'ML_BARTModel.R' 'ML_BlackBoostModel.R'
'ML_C50Model.R' 'ML_CForestModel.R' 'ML_CoxModel.R'
'ML_EarthModel.R' 'ML_FDAModel.R' 'ML_GAMBoostModel.R'
'ML_GBMModel.R' 'ML_GLMBoostModel.R' 'ML_GLMModel.R'
'ML_GLMNetModel.R' 'ML_KNNModel.R' 'ML_LARSModel.R'
'ML_LDAModel.R' 'ML_LMModel.R' 'ML_MDAModel.R' 'ML_NNetModel.R'
'ML_NaiveBayesModel.R' 'ML_ParsnipModel.R' 'ML_PLSModel.R'
'ML_POLRModel.R' 'ML_QDAModel.R' 'ML_RFSRCModel.R'
'ML_RPartModel.R' 'ML_RandomForestModel.R' 'ML_RangerModel.R'
'ML_SVMModel.R' 'ML_StackedModel.R' 'ML_SuperModel.R'
'ML_SurvRegModel.R' 'ML_TreeModel.R' 'ML_XGBModel.R'
'MachineShop-package.R' 'ModelFrame.R' 'ModelRecipe.R'
'ModelSpecification.R' 'ModeledInput.R' 'TrainedInputs.R'
'TrainedModels.R' 'TrainingParams.R' 'append.R' 'calibration.R'
'case_comps.R' 'coerce.R' 'combine.R' 'conditions.R'
'confusion.R' 'convert.R' 'data.R' 'dependence.R' 'diff.R'
'expand.R' 'extract.R' 'fit.R' 'grid.R' 'metricinfo.R'
'metrics.R' 'metrics_factor.R' 'metrics_numeric.R'
'modelinfo.R' 'models.R' 'performance.R' 'performance_curve.R'
'plot.R' 'predict.R' 'predictors.R' 'print.R' 'recipe_roles.R'
'reexports.R' 'resample.R' 'response.R' 'rfe.R' 'settings.R'
'step_kmeans.R' 'step_kmedoids.R' 'step_lincomp.R' 'step_sbf.R'
'step_spca.R' 'summary.R' 'survival.R' 'utils.R' 'varimp.R'