R Packages

Here is a list of R packages that I find useful:

ggplot2 - is a plotting system for R, based on the grammar of graphics, which tries to take the good parts of base and lattice graphics and none of the bad parts. (Sep 2011)

caret - is a set of functions that attempt to streamline the process for creating predictive models. The package contains tools for: data splitting, pre-processing, model tuning using resampling, variable importance estimation. (short for Classification And REgression Training) (Sep 2011)

randomForest - Classification and regression based on a forest of trees using random inputs. (Dec 2011)

ada - Performs discrete, real, and gentle boost under both exponential and logistic loss on a given data set. The package ada provides a straightforward, well-documented, and broad boosting routine for classification, ideally suited for small to moderate-sized data sets. (Dec 2011)

gbm - Generalized Boosted Regression Models.  This package implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, and AdaBoost exponential loss. (Dec 2011)

dummies - Create dummy/indicator variables flexibly and efficiently.  Expands factors, characters and other eligible classes into dummy/indicator variables.

multicore - Overcome R's inefficient CPU usage.  This package provides a way of running parallel computations in R on machines with multiple cores or CPUs. Jobs can share the entire initial workspace and it provides methods for results collection.