Package: JMbayes2 0.6-5

JMbayes2: Extended Joint Models for Longitudinal and Time-to-Event Data

Fit joint models for longitudinal and time-to-event data under the Bayesian approach. Multiple longitudinal outcomes of mixed type (continuous/categorical) and multiple event times (competing risks and multi-state processes) are accommodated. Rizopoulos (2012, ISBN:9781439872864).

Authors:Dimitris Rizopoulos [aut, cre], Pedro Miranda-Afonso [aut], Grigorios Papageorgiou [aut]

JMbayes2_0.6-5.tar.gz
JMbayes2_0.6-5.zip(r-4.7-x86_64)JMbayes2_0.6-5.zip(r-4.7-arm64)JMbayes2_0.6-5.zip(r-4.6-x86_64)JMbayes2_0.6-5.zip(r-4.6-arm64)JMbayes2_0.6-5.zip(r-4.5-x86_64)
JMbayes2_0.6-5.tgz(r-4.6-x86_64)JMbayes2_0.6-5.tgz(r-4.6-arm64)JMbayes2_0.6-5.tgz(r-4.5-x86_64)JMbayes2_0.6-5.tgz(r-4.5-arm64)
JMbayes2_0.6-5.tar.gz(r-4.7-arm64)JMbayes2_0.6-5.tar.gz(r-4.7-x86_64)JMbayes2_0.6-5.tar.gz(r-4.6-arm64)JMbayes2_0.6-5.tar.gz(r-4.6-x86_64)
JMbayes2_0.6-5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
JMbayes2/json (API)

# Install 'JMbayes2' in R:

install.packages('JMbayes2', repos = c('https://drizopoulos.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/drizopoulos/jmbayes2/issues

Pkgdown/docs site:https://drizopoulos.github.io

Uses libs:

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

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:

competing-risks longitudinal-analysis mixed-models multi-state personalized-medicine precision-medicine prediction-model survival-models openblas cpp openmp

9.41 score 106 stars 2 packages 389 scripts 1.4k downloads 55 exports 31 dependencies

Last updated from:184535cc85. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	159
linux-devel-x86_64	OK	200
source / vignettes	OK	400
linux-release-arm64	OK	206
linux-release-x86_64	OK	241
macos-release-arm64	OK	158
macos-release-x86_64	OK	360
macos-oldrel-arm64	OK	150
macos-oldrel-x86_64	OK	472
windows-devel-arm64	OK	295
windows-devel-x86_64	OK	233
windows-release-arm64	OK	311
windows-release-x86_64	OK	190
windows-oldrel-x86_64	OK	173
wasm-release	OK	154

Exports:acceleration area calibration_metrics calibration_plot coef coefs compare_jm consensus coxph create_folds crisk_setup cumuplot decision_curves Delta densplot Dexp Dexpit family fixef gelman_diag ggdensityplot ggtraceplot jm lme mixed_model model.frame model.matrix poly2 poly3 poly4 ppcheck ranef rc_setup slicer slope terms traceplot tv tvAUC tvBrier tvEC tvEPCE tvIWEC tvNB tvROC vabs value variogram velocity vexp vexpit vlog vlog10 vlog2 vsqrt

Dependencies:abind cli coda cpp11 farver ggplot2 GLMMadaptive glue gridExtra gtable isoband labeling lattice lifecycle MASS Matrix matrixStats nlme parallelly R6 RColorBrewer Rcpp RcppArmadillo rlang S7 scales survC1 survival vctrs viridisLite withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Didanosine versus Zalcitabine in HIV Patients	aids aids.id
Calibration Plot for Dynamic Predictions under Right-Censored Data	calibration_metrics calibration_plot calibration_plot.coxph calibration_plot.jm
Consensus Combination of Posterior Draws from Joint Model Fits	consensus
Creating Training and Testing Datasets	create_folds
Transform Competing Risks Data in Long Format	crisk_setup
(Un)weighted Decision Curves for Dynamic Predictions under Left-Truncated and Right-Censored Data	decision_curves decision_curves.coxph decision_curves.default decision_curves.jm plot.decision_curves
Joint Models for Longitudinal and Time-to-Event Data	acceleration area coefs Delta Dexp Dexpit jm poly2 poly3 poly4 slope tv vabs value velocity vexp vexpit vlog vlog10 vlog2 vsqrt
Various Methods for Functions from the _coda_ Package	coda_methods.jm cumuplot cumuplot.jm densplot densplot.jm gelman_diag gelman_diag.jm ggdensityplot ggdensityplot.jm ggtraceplot ggtraceplot.jm traceplot traceplot.jm
Various Methods for Standard Generics	coef coef.jm compare_jm family family.jm fixef fixef.jm get_links get_links.jm methods.jm model.frame model.frame.jm model.matrix model.matrix.jm ranef ranef.jm terms terms.jm
Extended Joint Models for Longitudinal and Time-to-Event Data	JMbayes2-package JMbayes2
Slice-aware model fitting generics	coxph lme mixed_model
Mayo Clinic Primary Biliary Cirrhosis Data	pbc2 pbc2.id
Posterior Predictive Checks for Joint Models	ppcheck
Predictions from Joint Models	plot.predict_jm predict.jm predict.jmList
Prednisone versus Placebo in Liver Cirrhosis Patients	prothro prothros
Combine Recurring and Terminal Event Data in Long Format	rc_setup
Split Longitudinal and Survival Data into Subject-level Samples	slicer
Area Under the Receiver Operating Characteristic Curve (AUC) for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvAUC tvAUC.coxph tvAUC.jm
Brier Score for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvBrier tvBrier.coxph tvBrier.jm
Expected Cost for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvEC tvEC.coxph tvEC.default tvEC.jm
Expected Predictive Cross Entropy for Dynamic Predictions under Right-Censored Data	tvEPCE
Integrated Weighted Expected Cost for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvIWEC tvIWEC.coxph tvIWEC.default tvIWEC.jm
Net Benefit for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvNB tvNB.coxph tvNB.default tvNB.jm
Receiver Operating Characteristic Curve (ROC) for Dynamic Predictions under Left-Truncated and Right-Censored Data	tvROC tvROC.coxph tvROC.jm
Variogram for Longitudinal Data	variogram