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. The random
                        intercept can be included in the model
                        formula. Both the error distribution and the
                        distribution of the random intercept is
                        expressed as a~penalized univariate normal
                        mixture with high number of components
                        (G-spline).
cgd                     Chronic Granulomatous Disease data
credible.region         Compute a simultaneous confidence region 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.
plot.bayesDensity       Plot an object of class bayesDensity
plot.bayesGspline       Plot an object of class 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.
sampleCovMat            Compute a sample covariance matrix.
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
