Fully Bayesian Classification with a 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.
|Depends:||R (≥ 2.13.1), abind|
|Maintainer:||Longhai Li <longhai at math.usask.ca>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||BCBCSF results|
|Windows binaries:||r-devel: BCBCSF_1.0-1.zip, r-release: BCBCSF_1.0-1.zip, r-oldrel: BCBCSF_1.0-1.zip|
|OS X El Capitan binaries:||r-release: BCBCSF_1.0-1.tgz|
|OS X Mavericks binaries:||r-oldrel: BCBCSF_1.0-1.tgz|
|Old sources:||BCBCSF archive|
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