The package supplies two efficients methodologies:
regularized CCA and sparse PLS to unravel relationships between
two heterogeneous data sets of size (nxp) and (nxq) where the p
and q variables are measured on the same samples or individuals
n. These data may come from high throughput technologies, such
as omics data (e.g. transcriptomics, metabolomics or proteomics
data) that require an integrative or joint analysis. However,
integrOmics can also be applied to any other large data sets
where p+q>>n. rCCA is a regularized version of CCA to deal with
the large number of variables. sPLS allows variable selection
in a one step procedure and two frameworks are proposed:
regression and canonical analysis. Numerous graphical outputs
are provided to help interpreting the results.

the integrOmics package is superseded by the mixOmics
package

integrate
2.1-2
FORTRAN by Alan Genz, S by Mike Meyer, R by Thomas Lumley and Martin Maechler
Adaptive Quadrature in up to 20 dimensions
Unclear (Fortran) -- code in Statlib's ./S/adapt

integrate
2.2-1
FORTRAN by Alan Genz, S by Mike Meyer, R by Thomas Lumley and Martin Maechler
Adaptive Quadrature in up to 20 dimensions
Unclear (Fortran) -- code in Statlib's ./S/adapt

integrate
2.2-2
numerical integration
FORTRAN by Alan Genz, S by Mike Meyer, R by Thomas Lumley and
Martin Maechler
Adaptive Quadrature in up to 20 dimensions
Unclear (Fortran) -- code in Statlib's ./S/adapt

FORTRAN by Alan Genz, S by Mike Meyer,
R by Thomas Lumley <thomas@biostat.washington.edu> and Martin Maechler

Mixture of experts models (Jacobs et al., 1991) were
introduced to account for nonlinearities and other complexities
in the data. It is based on a divide-and-conquer strategy.
Mixture of experts are of interest due to their wide
applicability and the advantages of fast learning via the
expectation-maximization (EM) algorithm. We have extended and
implemented mixture of experts to combine categorical clinical
factors and continuous microarray data in a binary
classification framework to analyze cancer studies. To provide
a hybrid signature of clinical factors and gene markers, we
propose to apply different gene selection procedures as a first
step.