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BayesPiecewiseICAR  

Hierarchical Bayesian Model for a Hazard Function
View on CRAN: Click here


Download and install BayesPiecewiseICAR package within the R console
Install from CRAN:
install.packages("BayesPiecewiseICAR")

Install from Github:
library("remotes")
install_github("cran/BayesPiecewiseICAR")

Install by package version:
library("remotes")
install_version("BayesPiecewiseICAR", "0.2.1")



Attach the package and use:
library("BayesPiecewiseICAR")
Maintained by
Andrew Chapple
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-07-27
Latest Update: 2023-08-18
Description:
Fits a piecewise exponential hazard to survival data using a Hierarchical Bayesian model with an Intrinsic Conditional Autoregressive formulation for the spatial dependency in the hazard rates for each piece. This function uses Metropolis- Hastings-Green MCMC to allow the number of split points to vary. This function outputs graphics that display the histogram of the number of split points and the trace plots of the hierarchical parameters. The function outputs a list that contains the posterior samples for the number of split points, the location of the split points, and the log hazard rates corresponding to these splits. Additionally, this outputs the posterior samples of the two hierarchical parameters, Mu and Sigma^2.
How to cite:
Andrew Chapple (2016). BayesPiecewiseICAR: Hierarchical Bayesian Model for a Hazard Function. R package version 0.2.1, https://cran.r-project.org/web/packages/BayesPiecewiseICAR. Accessed 21 Nov. 2024.
Previous versions and publish date:
0.1.0 (2016-07-27 19:00), 0.1.1 (2016-07-29 18:31), 0.2.0 (2016-08-20 10:31), 0.2.1 (2017-01-04 10:56)
Other packages that cited BayesPiecewiseICAR R package
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Other R packages that BayesPiecewiseICAR depends, imports, suggests or enhances
Functions, R codes and Examples using the BayesPiecewiseICAR R package
Some associated functions: ICARBHSampler . 
Some associated R codes: ICARBHSampler.R .  Full BayesPiecewiseICAR package functions and examples
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