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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.

Type Size Name Uploaded Downloads Labels
conda 736.9 kB | win-64/r-bcbcsf-1.0_1-r36hda5aaf8_0.tar.bz2  6 years and 1 month ago 6 main
conda 722.6 kB | osx-64/r-bcbcsf-1.0_1-r36h46e59ec_0.tar.bz2  6 years and 1 month ago 4 main
conda 722.8 kB | linux-64/r-bcbcsf-1.0_1-r36h96ca727_0.tar.bz2  6 years and 1 month ago 4 main

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