In An Empirical Comparison of Supervised Learning Algorithms (ICML 2006) the authors (Rich Caruana and Alexandru Niculescu-Mizil) evaluated several classification algorithms (SVMs, ANN, KNN, Random Forests, Decision Trees, etc.), and reported that calibrated boosted trees ranked as the best learning algorithm overall across eight different metrics (F-score, ROC Area, average precision, cross-entropy, etc.).
I would like to test calibrated boosted decision trees in one of my projects, and was wondering if anybody could suggest a good R package or MATLAB library for this.
I am relatively new to R, although I have large experience with MATLAB and Python. I have read about R's gbm, tree, and rpart but I am not sure if these packages implement calibrated boosted decision trees or if there are others that implement them.