# R¶

In this page we briefly discuss the R wrapper, which provides most of Optunity’s functionality.
For a general overview, we recommend reading the *User Guide*.

For installation instructions, please refer to *Installing Optunity*. To use the package has to be loaded like all R packages by:

```
library(optunity)
```

## Manual¶

All functions in R wrapper have documentation available in R’s help system,
e.g., ?cv.particle_swarm gives help for cross-validation (CV) using **particle swarms**.
To see the list of available functions type optunity::<TAB><TAB> in R’s command line.

For R following main functions are available:

- cv.setup for
**setting up CV**, specifying data and the number of folds - cv.particle_swarm, cv.nelder_mead, cv.grid_search, cv.random_search for
**optimizing hyperparameters**in CV setting. - auc_roc and auc_pr for calculating
**AUC**of ROC and precision-recall curve. - early_rie and early_bedroc for
**early discovery**metrics. - mean_se and mean_ae for regression
**loss functions**, mean squared error and mean absolute error. - particle_swarm, nelder_mead, grid_search, random_search for minimizing (or maximizing) regular functionals.
- cv.run performs a
**cross-validation**evaluation for the given values of the hyperparameters (no optimization).

General workflow is to first create a CV object using **cv.setup** and then use
it to run CV, by using functions like **cv.particle_swarms**, **cv.random_search** etc.

## Examples¶

Please see the example pages,
*Ridge Regression* and *SVM (e1071)*. The R help pages for each function
contain examples showing how to use them.