Description: the integrOmics package is superseded by the mixOmics

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

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

Author: FORTRAN by Alan Genz, S by Mike Meyer, R by Thomas Lumley and

Package: integrate
Version: 2.2-3
Title: numerical integration
Author: FORTRAN by Alan Genz, S by Mike Meyer,
R by Thomas Lumley <thomas@biostat.washington.edu> and Martin Maechler
Description: Adaptive Quadrature in up to 20 dimensions
Depends: R (<= 1.2.3)
License: Unclear (Fortran) -- code in Statlib's ./S/adapt

Package: integrativeME
Type: Package
Title: integrative mixture of experts
Version: 1.1
Date: 2009-09-29
Depends: mclust, integrOmics, randomForest
Author: Kim-Anh Le Cao
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
Description: 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.
License: GPL (>= 2)
Packaged: 2009-10-14 07:15:34 UTC; k.lecao
Repository: CRAN
Date/Publication: 2009-10-14 09:59:57

Package: integrativeME
Type: Package
Title: integrative mixture of experts
Version: 1.2
Date: 2009-09-29
Depends: R (>= 2.10), mclust, mixOmics, randomForest
Author: Kim-Anh Le Cao
Maintainer: Kim-Anh Le Cao <k.lecao@uq.edu.au>
Description: 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.
License: GPL (>= 2)
Packaged: 2012-07-23 10:09:21 UTC; ripley
Repository: CRAN
Date/Publication: 2012-07-23 10:35:32