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) and J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation for statistical parametric mapping, NeuroImage, 52:515-523 (2010).
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_ bath.ac.uk>, with contributions by Benjamin J. Cairns <email@example.com>. These numerical routines first appeared in Simon Wood's solv95 program.
The selection method uses either a greedy search or headlong search. The greedy search at each step either checks all variables not currently included in the set of clustering variables singly for inclusion into the set or checks all variables in the set of clustering variables singly for exclusion.The headlong search only checks until a variable is included or excluded (i.e. does not necessarily check all possible variables for inclusion/exclusion at each step) and any variable with evidence of clustering below a certain level at any stage is removed from consideration for the remainder of the algorithm. Each variable's evidence for being useful to the clustering given the currently selected clustering variables is given by the difference between the BIC for the model with clustering (allowed to vary over 2 to a maximum number of groups and any of the different covariance parameterizations allowed in mclust) using the set of clustering variables including the variable being checked and the sum of BICs for the model with clustering (allowed to vary over 2 to a maximum number of groups and any of the different covariance parameterizations allowed in mclust) using the set of clustering variables without the variable being checked and the model for the variable being checked being conditionally independent of the clustering given the other clustering variables (this is modeled as a regression of the variable being checked on the other clustering variables).