Package: BCBCSF
Version: 0.0-0
Title: Bias-corrected Bayesian Classification with Selected Features
Author: Longhai Li <longhai@math.usask.ca>
Maintainer: Longhai Li <longhai@math.usask.ca>
Depends: R (>= 2.12.1), abind
Description: This software is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.
License: GPL (>= 2)
URL: http://www.r-project.org, http://math.usask.ca/~longhai
Packaged: 2011-07-27 06:46:52 UTC; longhai
Repository: CRAN
Date/Publication: 2011-07-27 07:10:33

Package: BCBCSF
Version: 0.0-1
Title: Bias-corrected Bayesian Classification with Selected Features
Author: Longhai Li <longhai@math.usask.ca>
Maintainer: Longhai Li <longhai@math.usask.ca>
Depends: R (>= 2.12.1), abind
Description: This software is used to predict the discrete class labels
based on a selected subset of high-dimensional features, such
as expression levels of genes. The data are modeled with a
hierarchical Bayesian models using heavy-tailed t distributions
as priors. When a large number of features are available, one
may like to select only a subset of features to use, typically
those features strongly correlated with the response in
training cases. Such a feature selection procedure is however
invalid since the relationship between the response and the
features has be exaggerated by feature selection. This package
provides a way to avoid this bias and yield better-calibrated
predictions for future cases when one uses F-statistic to
select features.
License: GPL (>= 2)
URL: http://www.r-project.org, http://math.usask.ca/~longhai
Packaged: 2011-09-13 20:45:09 UTC; longhai
Repository: CRAN
Date/Publication: 2011-09-14 06:20:04