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

## Documentation for package ‘JMbayes’ version 0.9-0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

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