Package: JMbayes 0.8-86

JMbayes: Joint Modeling of Longitudinal and Time-to-Event Data under a Bayesian Approach

Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.

Authors:Dimitris Rizopoulos <[email protected]>

JMbayes_0.8-86.tar.gz
JMbayes_0.8-86.zip(r-4.7)JMbayes_0.8-86.zip(r-4.6)JMbayes_0.8-86.zip(r-4.5)
JMbayes_0.8-86.tgz(r-4.6-x86_64)JMbayes_0.8-86.tgz(r-4.6-arm64)JMbayes_0.8-86.tgz(r-4.5-x86_64)JMbayes_0.8-86.tgz(r-4.5-arm64)
JMbayes_0.8-86.tar.gz(r-4.7-arm64)JMbayes_0.8-86.tar.gz(r-4.7-x86_64)JMbayes_0.8-86.tar.gz(r-4.6-arm64)JMbayes_0.8-86.tar.gz(r-4.6-x86_64)
JMbayes_0.8-86.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
JMbayes/json (API)

# Install 'JMbayes' in R:

install.packages('JMbayes', repos = c('https://drizopoulos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/drizopoulos/jmbayes/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library
jags– Just Another Gibbs Sampler for Bayesian MCMC

Datasets:

aids - Didanosine versus Zalcitabine in HIV Patients
aids.id - Didanosine versus Zalcitabine in HIV Patients
pbc2 - Mayo Clinic Primary Biliary Cirrhosis Data
pbc2.id - Mayo Clinic Primary Biliary Cirrhosis Data
prothro - Prednisone versus Placebo in Liver Cirrhosis Patients
prothros - Prednisone versus Placebo in Liver Cirrhosis Patients

On CRAN:

joint-models longitudinal-responses prediction-model survival-analysis openblas cpp openmp jags

7.04 score 58 stars 94 scripts 505 downloads 17 mentions 35 exports 101 dependencies

Last updated from:ff8e560f1f. Checks:8 WARNING, 2 OK, 3 FAIL. Indexed: yes.

Target	Result	Time
linux-devel-arm64	WARNING	275
linux-devel-x86_64	WARNING	289
source / vignettes	OK	284
linux-release-arm64	WARNING	277
linux-release-x86_64	WARNING	294
macos-release-arm64	WARNING	161
macos-release-x86_64	WARNING	416
macos-oldrel-arm64	FAIL	73
macos-oldrel-x86_64	FAIL	183
windows-devel	WARNING	345
windows-release	WARNING	291
windows-oldrel	FAIL	82
wasm-release	OK	174

Exports:anova.JMbayes aucJM bma.combine coef.JMbayes cvDCL dbs dgt dns dynCJM dynInfo extract_lmeComponents find_thresholds fixef.JMbayes ibs IndvPred_lme ins jointModelBayes logLik.JMbayes marglogLik mvglmer mvJointModelBayes pgt plot.JMbayes plot.survfit.JMbayes prederrJM predict_eventTime predict.JMbayes qgt ranef.JMbayes rgt rocJM runDynPred survfitJM tve xtable.JMbayes

Dependencies:abind backports base64enc BH bslib cachem callr checkmate cli cluster coda codetools colorspace commonmark cpp11 data.table desc digest distributional doParallel evaluate farver fastmap fontawesome foreach foreign Formula fs generics ggplot2 glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets httpuv inline isoband iterators jagsUI jquerylib jsonlite knitr labeling later lattice lifecycle loo magrittr MASS Matrix matrixStats memoise mime nlme nnet numDeriv otel pillar pkgbuild pkgconfig posterior processx promises ps QuickJSR R6 rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppParallel rjags rlang rmarkdown rpart rstan rstudioapi S7 sass scales shiny sourcetools StanHeaders stringi stringr survival tensorA tibble tinytex utf8 vctrs viridisLite withr xfun xtable yaml

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Didanosine versus Zalcitabine in HIV Patients	aids aids.id
Anova Method for Fitted Joint Models	anova.JMbayes
Time-Dependent ROCs and AUCs for Joint Models	aucJM aucJM.JMbayes aucJM.mvJMbayes find_thresholds find_thresholds.mvJMbayes predict_eventTime predict_eventTime.mvJMbayes rocJM rocJM.JMbayes rocJM.mvJMbayes
Combines Predictions for Bayesian Model Averaging	bma.combine
Estimated Coefficients and Confidence Intervals for Joint Models	coef.JMbayes confint.JMbayes fixef.JMbayes
Dynamic Information	cvDCL
Derivatives and Integrals of B-splines and Natural Cubic splines	dbs dns ibs ins
A Dynamic Discrimination Index for Joint Models	dynCJM dynCJM.JMbayes
Dynamic Information of an Extra Longitudinal Measurement	dynInfo
Fitted Values and Residuals for Joint Models	fitted.JMbayes residuals.JMbayes
The Generalized Student's t Distribution	dgt pgt qgt rgt
Individualized Predictions from Linear Mixed Models	extract_lmeComponents IndvPred_lme
Joint Modeling of Longitudinal and Time-to-Event Data in R under a Bayesian Approach	JMbayes-package JMbayes
Fitted JMbayes Object	JMbayesObject
Joint Models for Longitudinal and Time-to-Event Data	jointModelBayes
Log-Likelihood for Joint Models	logLik.JMbayes
Calculates Marginal Subject-specific Log-Likelihood Contributions	marglogLik
Multivariate Mixed Models	mvglmer
Multivariate Joint Models for Longitudinal and Time-to-Event Data	mvJointModelBayes
Mayo Clinic Primary Biliary Cirrhosis Data	pbc2 pbc2.id
MCMC Diagnostics for Joint Models	plot.JMbayes
Plot Method for survfit.JMbayes and survfit.mvJMbayes Objects	plot.survfit.JMbayes plot.survfit.mvJMbayes
Prediction Errors for Joint Models	prederrJM prederrJM.JMbayes prederrJM.mvJMbayes
Predictions for Joint Models	predict.JMbayes
Prednisone versus Placebo in Liver Cirrhosis Patients	prothro prothros
Random Effects Estimates for Joint Models	ranef.JMbayes
Shiny Application for Dynamic Predictions	runDynPred
Prediction in Joint Models	survfitJM survfitJM.JMbayes survfitJM.mvJMbayes
Time-Varying Effects using P-splines	tve
xtable Method from Joint Models.	xtable.JMbayes