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bayesSurv (version 2.3-1)
Bayesian Survival Regression with Flexible Error and Random Effects Distributions
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Install
install.packages('bayesSurv')
Monthly Downloads
675
Version
2.3-1
License
GPL (>= 2)
Maintainer
Arnošt Komárek
Last Published
August 9th, 2014
Functions in bayesSurv (2.3-1)
Search functions
bayessurvreg1.help
Helping function for Bayesian survival regression models, version 1.
marginal.bayesGspline
Summary for the marginal density estimates based on the bivariate model with Bayesian G-splines.
bayessurvreg2.help
Helping functions for Bayesian regression with an error distribution smoothed using G-splines
credible.region
Compute a simultaneous credible region (rectangle) from a sample for a vector valued parameter.
give.init
Check and possibly fill in initial values for the G-spline, augmented observations and allocations for Bayesian models with G-splines
bayessurvreg1.files2init
Read the initial values for the Bayesian survival regression model to the list.
rWishart
Sample from the Wishart distribution
bayesBisurvreg.help
Helping function for Bayesian regression with smoothed bivariate densities as the error term, based on possibly censored data
bayesDensity
Summary for the density estimate based on the mixture Bayesian AFT model.
bayessurvreg3
Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution.
bayesHistogram.help
Helping function for Bayesian smoothing of (bi)-variate densities based on possibly censored data
files.Gspline
Write headers to or clean files with sampled G-spline
densplot2
Probability density function estimate from MCMC output
plot.bayesGspline
Plot an object of class bayesGspline
traceplot2
Trace plot of MCMC output.
cgd
Chronic Granulomatous Disease data
plot.bayesDensity
Plot an object of class bayesDensity
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).
sampled.kendall.tau
Estimate of the Kendall's tau from the bivariate model
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.
bayessurvreg1
A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of components
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.
vecr2matr
Transform single component indeces to double component indeces
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
files2coda
Read the sampled values from the Bayesian survival regression model to a coda mcmc object.
plot.marginal.bayesGspline
Plot an object of class marginal.bayesGspline
tandmob2
Signal Tandmobiel data, version 2
tandmobRoos
Signal Tandmobiel data, version Roos
bayessurvreg.help
Helping function for Bayesian survival regression models.
bayessurvreg3.help
Helping functions for Bayesian regression with an error distribution smoothed using G-splines
scanFN
Read Data Values
print.bayesDensity
Print a summary for the density estimate based on the Bayesian model.
rMVNorm
Sample from the multivariate normal distribution
simult.pvalue
Compute a simultaneous p-value from a sample for a vector valued parameter.
sampleCovMat
Compute a sample covariance matrix.
give.summary
Brief summary for the chain(s) obtained using the MCMC.