Bayesian Survival Regression with Flexible Error and Random Effects Distributions


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Documentation for package `bayesSurv' version 0.3-2

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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