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MachineShop
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", "3.7.0")
Attach the package and use:
library("MachineShop")
Maintained by
Brian J Smith
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-10-14
Latest Update: 2023-09-18
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 3.7.0, https://cran.r-project.org/web/packages/MachineShop
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)
Other packages that cited MachineShop R package
View MachineShop citation profile
Other R packages that MachineShop depends,
imports, suggests or enhances
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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