[R-pkgs] Major update: mboost 2.0-0 released

From: Benjamin Hofner <Benjamin.Hofner_at_imbe.med.uni-erlangen.de>
Date: Tue, 02 Feb 2010 10:19:42 +0100

Dear useRs,

we are happy to announce the release of mboost 2.0-0 on CRAN:


This version contains major updates and changes to the implementation of the main algorithm. Some slight changes to the user-interface where necessary. Please consult the manual and the list of CHANGES below.

The package 'mboost' (Model-based Boosting) implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates or regression trees as base-learners for fitting generalized linear, additive and interaction models to potentially high-dimensional data.

A big variety of models can be investigated using 'mboost' including survival models, expectile regression models, ordinal regression models as well as standard models such as simple linear models. In all cases, the predictor can be specified very flexible using linear, smooth, random and spatial effects as well as decision trees or an arbitrary combination suiting the intention of the researcher.

For more details and a nice example showing some of the functionality of 'mboost' see ?mboost-package.

We appreciate any feedback.

    mboost development team

                 CHANGES in `mboost' VERSION 2.0-0 (2010-02-01)

   o  generic implementation of component-wise functional gradient
      boosting in `mboost_fit', specialized code for linear,
      additive and interaction models removed

   o  new families available for ordinal, expectile and censored

   o  computations potentially based on package Matrix
      (reduces memory usage)

   o various speed improvements

   o added interface to extract selected base-learners (selected())

   o added interface for parallel computations in cvrisk with

      arbitrary packages (e.g. multicore, snow)

   o added "which" argument in predict and coef functions and improved

      usability of "which" in plot-function. Users can specify "which" as
      numeric value or as a character string

   o  added function cv() to generate matrices for k-fold
      cross-validation, subsampling and bootstrap

   o new function stabsel() for stability selection with error control

   o added function model.weights() to extract the weights

   o added interface to expand model by increasing mstop in


   o alternative definition of degrees of freedom available

   o Interface changes:

Dipl.-Stat. Benjamin Hofner

Institut für Medizininformatik, Biometrie und Epidemiologie
Friedrich-Alexander-Universität Erlangen-Nürnberg
Waldstr. 6 - 91054 Erlangen - Germany



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