Package: BayesReversePLLH 1.5

Andrew G Chapple

BayesReversePLLH: Fits the Bayesian Piecewise Linear Log-Hazard Model

Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.

Authors:Andrew G Chapple

BayesReversePLLH_1.5.tar.gz
BayesReversePLLH_1.5.tar.gz(r-4.5-noble)BayesReversePLLH_1.5.tar.gz(r-4.4-noble)
BayesReversePLLH_1.5.tgz(r-4.4-emscripten)BayesReversePLLH_1.5.tgz(r-4.3-emscripten)
BayesReversePLLH.pdf |BayesReversePLLH.html✨
BayesReversePLLH/json (API)

# Install 'BayesReversePLLH' in R:
install.packages('BayesReversePLLH', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 264 downloads 15 exports 2 dependencies

Last updated 2 years agofrom:4bd120c598. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 18 2024
R-4.5-linux-x86_64NOTEDec 18 2024

Exports:ApproxMeanBayesPiecewiseHazardBayesPiecewiseHazardCOVBayesPiecewiseHazardTrtBayesPiecewiseLinearLogHazardBayesPiecewiseLinearLogHazardCOVBayesPiecewiseLinearLogHazardTrtGetALLHazLogSlopeGetALLHazPieceGetALLSurvPEHGetALLSurvPLLHPostMeanHazLogSlopePostMeanHazPiecePostMeanSurvPEHPostMeanSurvPLLH

Dependencies:RcppRcppArmadillo

Readme and manuals

Help Manual

Help pageTopics
Returns the approximate restricted posterior mean survival for the PLLH model.ApproxMean
Samples from the PEH model without covariates.BayesPiecewiseHazard
Samples from the PEH Cox model with a patient covariate vector.BayesPiecewiseHazardCOV
Samples from the PEH Cox model with a patient covariate vector.BayesPiecewiseHazardTrt
Samples from the PLLH model without covariates.BayesPiecewiseLinearLogHazard
Samples from the PLLH Cox model with a patient covariate vector.BayesPiecewiseLinearLogHazardCOV
Samples from the PEH Cox model with a treatment indicator.BayesPiecewiseLinearLogHazardTrt
Computes the posterior distribution of hazard value for a vector x for the Piecewise Linear Log Hazard model (PLLH)GetALLHazLogSlope
Computes the posterior hazard values for a vector x for the Piecewise Exponential Hazard model (PEH)GetALLHazPiece
Computes the posterior distribution of survival probabilities for a vector x for the Piecewise Exponential Hazard model (PEH)GetALLSurvPEH
Computes posterior distribution of survival probabilities for a vector x for the Piecewise Linear Log Hazard model (PLLH)GetALLSurvPLLH
Computes the posterior mean hazard value for a vector x for the Piecewise Linear Log Hazard model (PLLH)PostMeanHazLogSlope
Computes the posterior mean hazard values for a vector x for the Piecewise Exponential Hazard model (PEH)PostMeanHazPiece
Computes the posterior mean survival probabilities for a vector x for the Piecewise Exponential Hazard model (PEH)PostMeanSurvPEH
Computes the posterior mean survival probabilities for a vector x for the Piecewise Linear Log Hazard model (PLLH)PostMeanSurvPLLH