Package: bayesSurv 3.8

Arnošt Komárek

bayesSurv: Bayesian Survival Regression with Flexible Error and Random Effects Distributions

Contains Bayesian implementations of the Mixed-Effects Accelerated Failure Time (MEAFT) models for censored data. Those can be not only right-censored but also interval-censored, doubly-interval-censored or misclassified interval-censored. The methods implemented in the package have been published in Komárek and Lesaffre (2006, Stat. Modelling) <doi:10.1191/1471082X06st107oa>, Komárek, Lesaffre and Legrand (2007, Stat. in Medicine) <doi:10.1002/sim.3083>, Komárek and Lesaffre (2007, Stat. Sinica) <https://www3.stat.sinica.edu.tw/statistica/oldpdf/A17n27.pdf>, Komárek and Lesaffre (2008, JASA) <doi:10.1198/016214507000000563>, García-Zattera, Jara and Komárek (2016, Biometrics) <doi:10.1111/biom.12424>.

Authors:Arnošt Komárek [aut, cre]

bayesSurv_3.8.tar.gz
bayesSurv_3.8.tar.gz(r-4.5-noble)bayesSurv_3.8.tar.gz(r-4.4-noble)
bayesSurv_3.8.tgz(r-4.4-emscripten)bayesSurv_3.8.tgz(r-4.3-emscripten)
bayesSurv.pdf |bayesSurv.html✨
bayesSurv/json (API)

# Install 'bayesSurv' in R:
install.packages('bayesSurv', 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
Datasets:
  • cgd - Chronic Granulomatous Disease data
  • tandmob2 - Signal Tandmobiel data, version 2
  • tandmobRoos - Signal Tandmobiel data, version Roos

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

83 exports 0.61 score 5 dependencies 1 mentions 49 scripts 583 downloads

Last updated 1 days agofrom:90e3bebc8d. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 17 2024
R-4.5-linux-x86_64NOTESep 17 2024

Exports:bayesBisurvregbayesBisurvreg.checkStorebayesBisurvreg.priorBetabayesBisurvreg.priorInitbayesBisurvreg.writeHeadersbayesDensitybayesGsplinebayesHistogrambayesHistogram.checkStorebayesHistogram.designbayesHistogram.priorInitbayesHistogram.writeHeadersbayessurvreg.checknsimulbayessurvreg.designbayessurvreg1bayessurvreg1.checkStorebayessurvreg1.files2initbayessurvreg1.priorbbayessurvreg1.priorBetabayessurvreg1.priorInitbayessurvreg1.revjumpbayessurvreg1.writeHeadersbayessurvreg2bayessurvreg2.checkStorebayessurvreg2.priorbbayessurvreg2.priorBetabayessurvreg2.priorInitbayessurvreg2.writeHeadersbayessurvreg3bayessurvreg3.checkrhobayessurvreg3.checkStorebayessurvreg3.priorbbayessurvreg3.priorBetabayessurvreg3.priorInitbayessurvreg3.priorinitNbbayessurvreg3.writeHeadersbayessurvreg3ParaC_bayesBisurvregC_bayesDensityC_bayesGsplineC_bayesHistogramC_bayessurvreg1C_bayessurvreg2C_choleskyC_findClosestKnotC_iPML_misclass_GJKC_marginal_bayesGsplineC_midimputeDataC_midimputeDataDoublyC_predictiveC_predictive_GSC_rmvnormR2006C_rwishartR3C_sampledKendallTauclean.Gsplinecredible.regiondensplot2files2codagive.init.Gsplinegive.init.rgive.init.ygive.init.y2give.summarymarginal.bayesGsplineplot.bayesDensityplot.bayesGsplineplot.marginal.bayesGsplinepredictivepredictive.controlpredictive2predictive2.controlpredictive2Paraprint.bayesDensityprint.simult.pvaluerMVNormrWishartsampleCovMatsampled.kendall.tauscanFNsimult.pvaluetraceplot2vecr2matrwrite.headers.Gspline

Dependencies:codalatticeMatrixsmoothSurvsurvival

Readme and manuals

Help Manual

Help pageTopics
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).bayesBisurvreg C_bayesBisurvreg
Summary for the density estimate based on the mixture Bayesian AFT model.bayesDensity C_bayesDensity
Summary for the density estimate based on the model with Bayesian G-splines.bayesGspline C_bayesGspline
Smoothing of a uni- or bivariate histogram using Bayesian G-splinesbayesHistogram C_bayesHistogram
A Bayesian survival regression with an error distribution expressed as a~normal mixture with unknown number of componentsbayessurvreg1 C_bayessurvreg1 C_cholesky
Read the initial values for the Bayesian survival regression model to the list.bayessurvreg1.files2init
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.bayessurvreg2 C_bayessurvreg2
Cluster-specific accelerated failure time model for multivariate, possibly doubly-interval-censored data with flexibly specified random effects and/or error distribution.bayessurvreg3 bayessurvreg3Para C_iPML_misclass_GJK
Chronic Granulomatous Disease datacgd
Compute a simultaneous credible region (rectangle) from a sample for a vector valued parameter.credible.region
Probability density function estimate from MCMC outputdensplot2
Read the sampled values from the Bayesian survival regression model to a coda mcmc object.files2coda
Brief summary for the chain(s) obtained using the MCMC.give.summary
Summary for the marginal density estimates based on the bivariate model with Bayesian G-splines.C_marginal_bayesGspline marginal.bayesGspline
Plot an object of class bayesDensityplot.bayesDensity
Plot an object of class bayesGsplineplot.bayesGspline
Plot an object of class marginal.bayesGsplineplot.marginal.bayesGspline
Compute predictive quantities based on a Bayesian survival regression model fitted using bayessurvreg1 function.C_predictive predictive predictive.control
Compute predictive quantities based on a Bayesian survival regression model fitted using bayesBisurvreg or bayessurvreg2 or bayessurvreg3 functions.C_predictive_GS predictive2 predictive2.control predictive2Para
Print a summary for the density estimate based on the Bayesian model.print.bayesDensity
Sample from the multivariate normal distributionC_rmvnormR2006 rMVNorm
Sample from the Wishart distributionC_rwishartR3 rWishart
Compute a sample covariance matrix.sampleCovMat
Estimate of the Kendall's tau from the bivariate modelC_sampledKendallTau sampled.kendall.tau
Read Data ValuesscanFN
Compute a simultaneous p-value from a sample for a vector valued parameter.print.simult.pvalue simult.pvalue
Signal Tandmobiel data, version 2tandmob2
Signal Tandmobiel data, version RoostandmobRoos
Trace plot of MCMC output.traceplot2
Transform single component indeces to double component indecesvecr2matr