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BayesPieceHazSelect

Variable Selection in a Hierarchical Bayesian Model for a Hazard Function

v1.1.0 · Jan 26, 2017 · GPL-2

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 and also uses Stochastic Search Variable Selection to determine what covariates drive the risk of the event. This function outputs trace plots depicting the number of split points in the hazard and the number of variables included in the hazard. The function saves all posterior quantities to the desired path.

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r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 NOTE
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r-patched-linux-x86_64 OK
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r-release-macos-x86_64 OK
r-release-windows-x86_64 OK
Check details (3 non-OK)
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LazyData

'LazyData' is specified without a 'data' directory
NOTE r-oldrel-macos-x86_64

LazyData

'LazyData' is specified without a 'data' directory
NOTE r-oldrel-windows-x86_64

LazyData

'LazyData' is specified without a 'data' directory

Check History

NOTE 11 OK · 3 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 10, 2026
NOTE r-oldrel-macos-arm64

LazyData

'LazyData' is specified without a 'data' directory
NOTE r-oldrel-macos-x86_64

LazyData

'LazyData' is specified without a 'data' directory
NOTE r-oldrel-windows-x86_64

LazyData

'LazyData' is specified without a 'data' directory

Dependency Network

Dependencies Reverse dependencies mvtnorm BayesPieceHazSelect

Version History

new 1.1.0 Mar 10, 2026