Bayesian Survival Regression with Flexible Error and Random Effects Distributions

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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