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internal

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.

internal: Internal functions used in package `gausspred'

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