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MachineShop  

Machine Learning Models and Tools
View on CRAN: Click here


Download and install MachineShop package within the R console
Install from CRAN:
install.packages("MachineShop")

Install from Github:
library("remotes")
install_github("cran/MachineShop")

Install by package version:
library("remotes")
install_version("MachineShop", "3.9.1")



Attach the package and use:
library("MachineShop")
Maintained by
Brian J Smith
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-10-14
Latest Update: 2025-06-09
Description:
Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.
How to cite:
Brian J Smith (2018). MachineShop: Machine Learning Models and Tools. R package version 3.9.1, https://cran.r-project.org/web/packages/MachineShop. Accessed 25 Jul. 2026.
Previous versions and publish date:
0.1-1 (2018-10-14 17:30), 0.2.0 (2018-11-19 17:30), 0.3.0 (2018-11-23 12:20), 0.4.0 (2018-12-13 00:20), 1.0.0 (2019-01-02 15:30), 1.1.0 (2019-01-23 15:10), 1.2.0 (2019-02-15 17:20), 1.3.0 (2019-04-23 16:00), 1.4.0 (2019-06-08 06:20), 1.5.0 (2019-08-01 19:30), 1.6.0 (2019-10-11 00:30), 2.0.0 (2019-12-10 23:40), 2.1.0 (2020-02-09 00:50), 2.2.0 (2020-03-18 16:40), 2.3.0 (2020-05-14 01:40), 2.4.0 (2020-06-05 00:40), 2.5.0 (2020-08-06 01:10), 2.6.0 (2021-01-19 20:20), 2.6.1 (2021-01-26 18:40), 2.7.0 (2021-03-02 20:10), 2.8.0 (2021-04-16 18:50), 2.9.0 (2021-06-18 10:20), 3.0.0 (2021-08-19 22:20), 3.1.0 (2021-10-01 16:00), 3.2.0 (2021-12-06 16:10), 3.3.0 (2022-02-09 14:20), 3.4.0 (2022-03-16 13:30), 3.5.0 (2022-06-03 10:40), 3.6.0 (2022-09-05 17:20), 3.6.1 (2023-02-01 19:40), 3.6.2 (2023-03-21 14:00), 3.7.0 (2023-09-18 16:00), 3.8.0 (2024-08-19 19:40), 3.9.0 (2025-06-09 21:10), 3.9.1 (2025-12-16 07:20), 3.9.2 (2026-01-30 16:40), (2026-07-09 08:10)
Other packages that cited MachineShop R package
View MachineShop citation profile
Other R packages that MachineShop depends, imports, suggests or enhances
Complete documentation for MachineShop
Functions, R codes and Examples using the MachineShop R package
Some associated functions: AdaBagModel . AdaBoostModel . BARTMachineModel . BARTModel . BlackBoostModel . C50Model . CForestModel . CoxModel . DiscreteVariate . EarthModel . FDAModel . GAMBoostModel . GBMModel . GLMBoostModel . GLMModel . GLMNetModel . ICHomes . KNNModel . LARSModel . LDAModel . LMModel . MDAModel . MLControl . MLMetric . MLModel . MachineShop-package . ModelFrame-methods . ModelSpecification-methods . NNetModel . NaiveBayesModel . PLSModel . POLRModel . ParameterGrid . ParsnipModel . QDAModel . RFSRCModel . RPartModel . RandomForestModel . RangerModel . SVMModel . SelectedInput . SelectedModel . StackedModel . SuperModel . SurvMatrix . SurvRegModel . TreeModel . TunedInput . TunedModel . TuningGrid . XGBModel . as.MLInput . as.MLModel . as.data.frame . calibration . case_weights . combine-methods . confusion . dependence . diff-methods . expand_model . expand_modelgrid-methods . expand_params . expand_steps . extract-methods . fit-methods . inputs . lift . metricinfo . metrics . modelinfo . models . performance . performance_curve . plot-methods . predict . print-methods . quote . recipe_roles . reexports . resample-methods . response-methods . rfe-methods . set_monitor-methods . set_optim-methods . set_predict . set_strata . settings . step_kmeans . step_kmedoids . step_lincomp . step_sbf . step_spca . summary-methods . t.test . unMLModelFit . varimp . 
Some associated R codes: 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_PLSModel.R . ML_POLRModel.R . ML_ParsnipModel.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 . TrainedInputs.R . TrainedModels.R . TrainingParams.R . append.R . calibration.R . case_comps.R . classes.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 . 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 .  Full MachineShop package functions and examples
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