Package: mkssd
Version: 1.1
Date: 2011-08-04
Title: Efficient multi-level k-circulant supersaturated designs
Author: B N Mandal <mandal.stat@gmail.com>
Maintainer: B N Mandal <mandal.stat@gmail.com>
Depends: R(>= 2.13.0)
Description: mkssd is a package that generates efficient balanced
non-aliased multi-level k-circulant supersaturated designs by
interchanging the elements of the generator vector. The package
tries to generate a supersaturated design that has chisquare
efficiency more than user specified efficiency level (mef). The
package also displays the progress of generation of an
efficient multi-level k-circulant design through a progress
bar. The progress of 100% means that one full round of
interchange is completed. More than one full round (typically
4-5 rounds) of interchange may be required for larger designs.
License: GPL (>= 2)
Packaged: 2011-08-05 06:53:22 UTC; User
Repository: CRAN
Date/Publication: 2011-08-05 08:18:15

Title: Efficient Multi-Level k-Circulant Supersaturated Designs
Description: Generates efficient balanced non-aliased multi-level k-circulant supersaturated designs by interchanging the elements of the generator vector. Attempts to generate a supersaturated design that has chisquare efficiency more than user specified efficiency level (mef). Displays the progress of generation of an efficient multi-level k-circulant design through a progress bar. The progress of 100% means that one full round of interchange is completed. More than one full round (typically 4-5 rounds) of interchange may be required for larger designs.

Title: Copula selection and fitting using maximum likelihood
Author: Jesus Garcia and Veronica Gonzalez-Lopez
Description: Use numerical maximum likelihood to choose and fit a bivariate copula model (from a library of 40 models) to the data.

Title: Machine Learning-based Differential Network Analysis of
Depends: R (>= 2.15.1), biwt, cairoDevice, e1071, fBasics,
GeneSelector, grDevices, gplots, gWidgets, gWidgetsRGtk2, hash,
igraph, nnet, parmigene, pROC, qvalue, ROCR, rsgcc, stringr,
Description: Functions necessary to perform the machine learning-based
differential network analysis of transcriptome data.