R: Bayesian Survival Regression with Flexible Error and Random Effects Distributions

# Bayesian Survival Regression with Flexible Error and Random Effects Distributions

## Documentation for package ‘bayesSurv’ version 3.8

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

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 | 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.
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