gbm (which stands for generalized boosted models) implements extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. It includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (i.e., LambdaMart).
Note: This is a maintained version of gbm back compatible to CRAN versions of gbm 2.1.x. It exists mainly for the purpose of reproducible research and data analyses performed with the 2.1.x versions of gbm. For newer development, and a more consistent API, try out the newer gbm3 package!