The caret package (short for Classification And REgression Training) attempts to streamline the process for creating predictive models. The package contains tools for: - data splitting - pre-processing - feature selection - model tuning using resampling (with parallel processing) - variable importance estimation as well as other functionality. The package website has lengthy descriptions of functionality: http://caret.r-forge.r-project.org/ A new version, 6.0-21, was just released to CRAN and contains major improvements: - A new system for user-defined models has been added. See http://caret.r-forge.r-project.org/custom_models.html. - When creating the grid of tuning parameter values, the column names no longer need to be preceded by a period. Periods can still be used as before but are not required. - trainControl now has a 'method = "none"' resampling option that bypasses model tuning and fits the model to the entire training set. - Several new models were added, bringing the total number of models up to 149. - ggplot methods were added for several classes. Most changes are not user-visible so that the impact on current scripts should be minimal. For this caret update, the AppliedPredictiveModeling package will be brought up to date shortly with slightly modified scripts for the analyses contained in the text. Thanks to the R community and caret contributors: Jed Wing, Steve Weston, Andre Williams, Chris Keefer, Allan Engelhardt, Tony Cooper, Zachary Mayer and the R Core Team. Please contact me (max.kuhn at pfizer.com) with any comments, suggestions, questions or ideas for a good wedding toasts. Max _______________________________________________ R-packages mailing list R-packages at r-project.org https://stat.ethz.ch/mailman/listinfo/r-packages