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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.
| Version: | 1.0-1 |
| Depends: | R (≥ 2.13.1), abind |
| Published: | 2015-09-26 |
| 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 |
| Reference manual: | BCBCSF.pdf |
| Package source: | BCBCSF_1.0-1.tar.gz |
| Windows binaries: | r-devel: BCBCSF_1.0-1.zip, r-release: BCBCSF_1.0-1.zip, r-oldrel: BCBCSF_1.0-1.zip |
| macOS binaries: | r-release (arm64): BCBCSF_1.0-1.tgz, r-oldrel (arm64): BCBCSF_1.0-1.tgz, r-release (x86_64): BCBCSF_1.0-1.tgz, r-oldrel (x86_64): BCBCSF_1.0-1.tgz |
| Old sources: | BCBCSF archive |
| Reverse imports: | HTLR |
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