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. |

bayessurvreg3Para | 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. |

C_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). |

C_bayesDensity | Summary for the density estimate based on the mixture Bayesian AFT model. |

C_bayesGspline | Summary for the density estimate based on the model with Bayesian G-splines. |

C_bayesHistogram | Smoothing of a uni- or bivariate histogram using Bayesian G-splines |

C_bayessurvreg1 | A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components |

C_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. |

C_cholesky | A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components |

C_iPML_misclass_GJK | Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution. |

C_marginal_bayesGspline | Summary for the marginal density estimates based on the bivariate model with Bayesian G-splines. |

C_predictive | Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function. |

C_predictive_GS | Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions. |

C_rmvnormR2006 | Sample from the multivariate normal distribution |

C_rwishartR3 | Sample from the Wishart distribution |

C_sampledKendallTau | Estimate of the Kendall's tau from the bivariate model |

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. |

predictive.control | 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. |

predictive2.control | Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions. |

predictive2Para | |

print.bayesDensity | Print a summary for the density estimate based on the Bayesian model. |

print.simult.pvalue | Compute a simultaneous p-value from a sample for a vector valued parameter. |

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 |