BCBCSF: Bias-corrected Bayesian Classification with Selected Features

This package 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.

Version: 1.0-0
Depends: R (≥ 2.13.1), abind
Published: 2013-01-18
Author: Longhai Li
Maintainer: Longhai Li <longhai at math.usask.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://www.r-project.org, http://math.usask.ca/~longhai
NeedsCompilation: yes
In views: Bayesian
CRAN checks: BCBCSF results

Downloads:

Reference manual: BCBCSF.pdf
Package source: BCBCSF_1.0-0.tar.gz
OS X binary: BCBCSF_1.0-0.tgz
Windows binary: BCBCSF_1.0-0.zip
Old sources: BCBCSF archive