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Regression subset selection including exhaustive search


Supervised and unsupervised self-organising maps


This package performs a homogeneity analysis and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.


The package contains R-functions to perform an fmri analysis as described in K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny, Analysing fMRI experiments with structure adaptive smoothing procedures, NeuroImage, 33:55-62 (2006), J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52:515-523 (2010) and K. Tabelow, J. Polzehl, Statistical Parametric Maps for Functional MRI Experiments in {R}: The Package {fmri}}, Journal of Statistical Software, 44(11):1--21 (2011).


Implementation of FastICA algorithm to perform Independent Component Analysis (ICA) and Projection Pursuit.


This package provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for estimating sparse Principal Components. The Lasso solution paths can be computed by the same function. First version: 2005-10.


Likelihood-based marginal regression and association modelling for repeated, or otherwise clustered, categorical responses using dependence ratio as a measure of the association


Analysis of dose-response curves in biology, environmental sciences, medicine, pharmacology, toxicology


***** This package is DEPRECATED. ***** Download the package 'PBSddesolve' to solve systems of delay differential equations using numerical routines written by Simon N. Wood <s.wood _at_>, with contributions by Benjamin J. Cairns <>. These numerical routines first appeared in Simon Wood's solv95 program.


A function which implements variable selection methodology for model-based clustering which allows to find the (locally) optimal subset of variables in a dataset that have group/cluster information. A greedy or headlong search can be used, either in a forward-backward or backward-forward direction, with or without sub-sampling at the hierarchical clustering stage for starting Mclust models. By default the algorithm uses a sequential search, but parallelization is also available.