calgb.pred

Same as 'calgb' for future patients (for prediction).

datasets

This package contains functions to perform inference via
        simulation from the posterior distributions for Bayesian
        nonparametric and semiparametric models. Although the name of
        the package was motivated by the Dirichlet Process prior, the
        package considers and will consider other priors on functional
        spaces. So far, DPpackage includes models considering Dirichlet
        Processes, Dependent Dirichlet Processes, Dependent Poisson-
        Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
        Trees, Mixtures of Triangular distributions, and Random
        Bernstein polynomials priors. The package also includes models
        considering Penalized B-Splines. Currently the package includes
        semiparametric models for marginal and conditional density
        estimation, ROC curve analysis, interval censored data, binary
        regression models, generalized linear mixed models, IRT type
        models, and generalized additive models. The package also
        contains functions to compute Pseudo-Bayes factors for model
        comparison, and to elicitate the precision parameter of the
        Dirichlet Process. To maximize computational efficiency, the
        actual sampling for each model is done in compiled FORTRAN. The
        functions return objects which can be subsequently analyzed
        with functions provided in the coda package.

calgb.pred: Cancer and Leukemia Group B (CALGB) Data for Prediction

Description

Usage

Arguments

Examples