bayesBisurvreg          Population-averaged accelerated failure time
                        model for bivariate, possibly
                        doubly-interval-censored data. The error
                        distribution is expressed as a penalized
                        bivariate normal mixture with high number of
                        components (bivariate G-spline).
bayesDensity            Summary for the density estimate based on the
                        mixture Bayesian AFT model.
bayesGspline            Summary for the density estimate based on the
                        model with Bayesian G-splines.
bayesHistogram          Smoothing of a uni- or bivariate histogram
                        using Bayesian G-splines
bayessurvreg1           A Bayesian survival regression with an error
                        distribution expressed as a~normal mixture with
                        unknown number of components
bayessurvreg1.files2init
                        Read the initial values for the Bayesian
                        survival regression model to the list.
bayessurvreg2           Cluster-specific accelerated failure time model
                        for multivariate, possibly
                        doubly-interval-censored data. The error
                        distribution is expressed as a penalized
                        univariate normal mixture with high number of
                        components (G-spline). The distribution of the
                        vector of random effects is multivariate
                        normal.
bayessurvreg3           Cluster-specific accelerated failure time model
                        for multivariate, possibly
                        doubly-interval-censored data with flexibly
                        specified random effects and/or error
                        distribution.
cgd                     Chronic Granulomatous Disease data
credible.region         Compute a simultaneous credible region
                        (rectangle) from a sample for a vector valued
                        parameter.
densplot2               Probability density function estimate from MCMC
                        output
files2coda              Read the sampled values from the Bayesian
                        survival regression model to a coda mcmc
                        object.
give.summary            Brief summary for the chain(s) obtained using
                        the MCMC.
marginal.bayesGspline   Summary for the marginal density estimates
                        based on the bivariate model with Bayesian
                        G-splines.
plot.bayesDensity       Plot an object of class bayesDensity
plot.bayesGspline       Plot an object of class bayesGspline
plot.marginal.bayesGspline
                        Plot an object of class marginal.bayesGspline
predictive              Compute predictive quantities based on a
                        Bayesian survival regression model fitted using
                        bayessurvreg1 function.
predictive2             Compute predictive quantities based on a
                        Bayesian survival regression model fitted using
                        bayesBisurvreg or bayessurvreg2 or
                        bayessurvreg3 functions.
print.bayesDensity      Print a summary for the density estimate based
                        on the Bayesian model.
rMVNorm                 Sample from the multivariate normal
                        distribution
rWishart                Sample from the Wishart distribution
sampleCovMat            Compute a sample covariance matrix.
sampled.kendall.tau     Estimate of the Kendall's tau from the
                        bivariate model
scanFN                  Read Data Values
simult.pvalue           Compute a simultaneous p-value from a sample
                        for a vector valued parameter.
tandmob2                Signal Tandmobiel data, version 2
tandmobRoos             Signal Tandmobiel data, version Roos
traceplot2              Trace plot of MCMC output.
vecr2matr               Transform single component indeces to double
                        component indeces
