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bayesSurv (version 0.5-10)

Bayesian Survival Regression with Flexible Error and Random Effects Distributions

Description

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Install

install.packages('bayesSurv')

Monthly Downloads

243

Version

0.5-10

License

GPL (>= 2)

Maintainer

Arnošt Komárek

Last Published

September 16th, 2024

Functions in bayesSurv (0.5-10)

bayessurvreg.help

Helping function for Bayesian survival regression models.

bayessurvreg1.help

Helping function for Bayesian survival regression models, version 1.

print.bayesDensity

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

credible.region

Compute a simultaneous credible region (rectangle) from a sample for a vector valued parameter.

give.summary

Brief summary for the chain(s) obtained using the MCMC.

bayesGspline

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

densplot2

Probability density function estimate from MCMC output

files.Gspline

Write headers to or clean files with sampled G-spline

bayessurvreg1

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

plot.bayesGspline

Plot an object of class bayesGspline

bayessurvreg1.files2init

Read the initial values for the Bayesian survival regression model to the list.

bayesHistogram

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

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).

cgd

Chronic Granulomatous Disease data

bayessurvreg2.help

Helping functions for Bayesian regression with an error distribution smoothed using G-splines

bayessurvreg3

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

bayessurvreg3.help

Helping functions for Bayesian regression with an error distribution smoothed using G-splines

bayesHistogram.help

Helping function for Bayesian smoothing of (bi)-variate densities based on possibly censored data

bayesDensity

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

bayesBisurvreg.help

Helping function for Bayesian regression with smoothed bivariate densities as the error term, based on possibly censored data

traceplot2

Trace plot of MCMC output.

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.

sampled.kendall.tau

Estimate of the Kendall's tau from the bivariate model

marginal.bayesGspline

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

rMVNorm

Sample from the multivariate normal distribution

files2coda

Read the sampled values from the Bayesian survival regression model to a coda mcmc object.

vecr2matr

Transform single component indeces to double component indeces

predictive

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

tandmobRoos

Signal Tandmobiel data, version Roos

give.init

Check and possibly fill in initial values for the G-spline, augmented observations and allocations for Bayesian models with G-splines

predictive2

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

simult.pvalue

Compute a simultaneous p-value from a sample for a vector valued parameter.

rWishart

Sample from the Wishart distribution

plot.bayesDensity

Plot an object of class bayesDensity

scanFN

Read Data Values

sampleCovMat

Compute a sample covariance matrix.

tandmob2

Signal Tandmobiel data, version 2

plot.marginal.bayesGspline

Plot an object of class marginal.bayesGspline