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

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Documentation for package ‘JMbayes’ version 0.8-85

• 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.