Connect R with MOA for Massive Online Analysis (RMOA)
Connect R with MOA (Massive Online Analysis – to build classification models and regression models on streaming data or out-of-RAM data

Visualizing the Performance of Scoring Classifiers (ROCR)
ROC graphs, sensitivity/specificity curves, lift charts, and precision/recall plots are popular examples of trade-off visualizations for specific pairs of performance measures. ROCR is a flexible tool for creating cutoff-parameterized 2D performance curves by freely combining two from over 25 performance measures (new performance measures can be added using a standard interface). Curves from different cross-validation or bootstrapping runs can be averaged by different methods, and standard deviations, standard errors or box plots can be used to visualize the variability across the runs. The parameterization can be visualized by printing cutoff values at the corresponding curve positions, or by coloring the curve according to cutoff. All components of a performance plot can be quickly adjusted using a flexible parameter dispatching mechanism. Despite its flexibility, ROCR is easy to use, with only three commands and reasonable default values for all optional parameters.

Fast Searches for Interactions (FSInteract)
Performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in ‘Shah, R. D. and Meinshausen, N. (2014) Random Intersection Trees’. The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1’s.