Other packages > Find by keyword >

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 14 Jun. 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)
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

manipulate  
Interactive Plots for RStudio
Interactive plotting functions for use within RStudio.The manipulate function accepts a plot ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
fourPNO  
Bayesian 4 Parameter Item Response Model
Estimate Barton & Lord's (1981) four parameter IRT model w ...
Download / Learn more Package Citations See dependency  
MGBT  
Multiple Grubbs-Beck Low-Outlier Test
Compute the multiple Grubbs-Beck low-outlier test on positively distributed data and utilities for ...
Download / Learn more Package Citations See dependency  
rCRM  
Regularized Continual Reassessment Method
Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), ...
Download / Learn more Package Citations See dependency  
rpymat  
Easy to Configure an Isolated 'Python' Environment
Aims to create a single isolated 'Miniconda' and 'Python' environment for reproducible pipeline scr ...
Download / Learn more Package Citations See dependency  

27,457

R Packages

233,548

Dependencies

73,054

Author Associations

27,205

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA