**MachineShop** is a meta-package for statistical and machine learning
with a unified interface for model fitting, prediction, performance
assessment, and presentation of results. Support is provided for
predictive modeling of numerical, categorical, and censored
time-to-event outcomes and for resample (bootstrap, cross-validation,
and split training-test sets) estimation of model performance. This
vignette introduces the package interface with a survival data analysis
example, followed by supported methods of variable specification;
applications to other response variable types; available performance
metrics, resampling techniques, and graphical and tabular summaries; and
modeling strategies.

- Unified and concise interface for model fitting, prediction, and performance assessment.
- Current support for 52 established models from 27
**R**packages. - Dynamic model parameters.
- Ensemble modeling with stacked regression and super learners.
- Modeling of response variables types: binary factors, multi-class nominal and ordinal factors, numeric vectors and matrices, and censored time-to-event survival.
- Model specification with traditional formulas, design matrices, and flexible pre-processing recipes.
- Resample estimation of predictive performance, including cross-validation, bootstrap resampling, and split training-test set validation.
- Parallel execution of resampling algorithms.
- Choices of performance metrics: accuracy, areas under ROC and precision recall curves, Brier score, coefficient of determination (R2), concordance index, cross entropy, F score, Gini coefficient, unweighted and weighted Cohenâ€™s kappa, mean absolute error, mean squared error, mean squared log error, positive and negative predictive values, precision and recall, and sensitivity and specificity.
- Graphical and tabular performance summaries: calibration curves, confusion matrices, partial dependence plots, performance curves, lift curves, and variable importance.
- Model tuning over automatically generated grids of parameter values and randomly sampled grid points.
- Model selection and comparisons for any combination of models and model parameter values.
- User-definable models and performance metrics.

```
# Current release from CRAN
install.packages("MachineShop")
# Development version from GitHub
# install.packages("devtools")
devtools::install_github("brian-j-smith/MachineShop")
# Development version with vignettes
devtools::install_github("brian-j-smith/MachineShop", build_vignettes = TRUE)
```

Once installed, the following **R** commands will load the package and
display its help system documentation. Online documentation and examples
are available at the MachineShop
website.

```
library(MachineShop)
# Package help summary
?MachineShop
# Vignette
RShowDoc("Introduction", package = "MachineShop")
```

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