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:
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')) |
- 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.
Last updated 3 months agofrom:90e3bebc8d. Checks:OK: 2. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 16 2024 |
R-4.5-linux-x86_64 | OK | Nov 16 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