Dear useRs,
we are happy to announce the release of mboost 2.0-0 on CRAN:
http://cran.r-project.org/package=mboost
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
regression
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
model[mstop]
o alternative definition of degrees of freedom available
o Interface changes:
- class definition / Family() arguments changed
- changed behavior of subset method (model[mstop]). Object
is directly altered and not duplicated
- argument "center" in bols replaced with "intercept"
- argument "z" in base-learners replaced with "by"
- bns and bss deprecated
--
******************************************************************************
Dipl.-Stat. Benjamin Hofner
Institut für Medizininformatik, Biometrie und Epidemiologie
Friedrich-Alexander-Universität Erlangen-Nürnberg
Waldstr. 6 - 91054 Erlangen - Germany
benjamin.hof...@imbe.med.uni-erlangen.de
http://www.imbe.med.uni-erlangen.de/~hofnerb/
http://www.benjaminhofner.de
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