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|                                                 |                  Cox                  |                 Shared                |                 Nested                |                Additive               |Joint standard *(Bivariate: 1 RE + 1 TE)*| Joint cluster *(Bivariate: 1 RE + 1 TE)*|Joint general *(Bivariate: 1 RE + 1 TE)*|Joint nested *(Bivariate: 1 RE + 1 TE)*|Joint longitudinal *(Bivariate:  1 LO + 1 TE)*|Joint trivariate *(Trivariate: 1 LO + 1 RE + 1 TE)*|Joint non linear trivariate *(Trivariate: 1 LO + 1 RE + 1 TE)*|Joint Multivariate *(Trivariate: 2 RE + 1 TE)*|Joint surrogate *(Bivariate: 2 TE)*| 
|:------------------------------------------------|:-------------------------------------:|:-------------------------------------:|:-------------------------------------:|:-------------------------------------:|:--------------------------------------:|:--------------------------------------:|:-------------------------------------:|:------------------------------------:|:------------------------------------------:|:-------------------------------------:|:-------------------------------------:|:-------------------------------------:|:-------------------------------------:|
|  `Available  options `                          |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |                                       |                                  
| Gamma distribution                              |                                       | <img src="blue_x.png"  title="frailtyPenal(Surv(time,status)~cluster(id)+sex+age,n.knots=12,kappa=10000,data=kidney)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+cov2,data=dataNested,n.knots=8,kappa=50000)"/>                       |                                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+sex+dukes+charlson+terminal(death),formula.terminalEvent=~sex+dukes+charlson,data=readmission,n.knots=14,kappa=c(9.55e+9,1.41e+12),recurrentAG=FALSE) "/>                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(group)+num.id(id)+dukes+charlson+sex+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission2,recurrentAG=FALSE,n.knots=8,kappa=c(1.e+10,1.e+10),Alpha=&quot;None&quot;)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+dukes+charlson+sex+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission,jointGeneral=TRUE,n.knots=8,kappa=c(2.11e+08,9.53e+11))"/>                       | <img src="blue_x.png"  title="frailtyPenal(formula=Surv(time,event)~subcluster(id)+cluster(group)+dukes+terminal(death),formula.terminalEvent=~dukes,data=readmissionNested,recurrentAG=FALSE,n.knots=8,kappa=c(9.55e+9,1.41e+12),initialize=TRUE,init.Alpha=1.091,Ksi=&quot;None&quot;)"/>                      |                                       |                                            |                                       |                                       |                                       |                       
| Log-Normal distribution                         |                                       | <img src="blue_x.png"  title="frailtyPenal(Surv(time,status)~cluster(id)+sex+age,n.knots=12,kappa=10000,data=kidney,RandDist=&quot;LogN&quot;)"/>                       |                                       | <img src="blue_x.png"  title="additivePenal(Surv(t1,t2,event)~cluster(group)+var1+slope(var1),correlation=TRUE,data=dataAdditive,n.knots=8,kappa=10000)"/>                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~cluster(id)+sex+dukes+charlson+terminal(death),formula.terminalEvent=~sex+dukes+charlson,data=readmission,n.knots=10,kappa=c(9.55e9,1.41e12),recurrentAG=TRUE,RandDist=&quot;LogN&quot;) "/>                       |                                        |                                       |                                      | <img src="blue_x.png"  title="longiPenal(Surv(time1,state)~age+treatment+who.PS+prev.resection,tumor.size~year*treatment+age+who.PS,colorectalSurv,data.Longi=colorectalLongi,random=c(&quot;1&quot;,&quot;year&quot;),id=&quot;id&quot;,link=&quot;Random-effects&quot;,left.censoring=-3.33,n.knots=7,kappa=2)"/>                            | <img src="blue_x.png"  title="trivPenal(Surv(time0,time1,new.lesions)~cluster(id)+age+treatment+who.PS+terminal(state),formula.terminalEvent=~age+treatment+who.PS+prev.resection,tumor.size~year*treatment+age+who.PS,data=colorectal,data.Longi=colorectalLongi,random=c(&quot;1&quot;,&quot;year&quot;),id=&quot;id&quot;,link=&quot;Random-effects&quot;,left.censoring=-3.33,recurrentAG=TRUE,n.knots=6,kappa=c(0.01,2),method.GH=&quot;Standard&quot;,n.nodes=7)"/>                       | <img src="blue_x.png"  title="trivPenalNL(Surv(time0,time1,new.lesions)~cluster(id)+age+treatment+terminal(state),formula.terminalEvent=~age+treatment,biomarker=&quot;tumor.size&quot;,formula.KG~1,formula.KD~treatment,dose=&quot;dose&quot;,time.biomarker=&quot;year&quot;,data=colorectal,data.Longi=colorectalLongi,random=&quot;KG&quot;,id=&quot;id&quot;,recurrentAG=TRUE,n.knots=5,kappa=c(0.01,2),method.GH=&quot;Pseudo-adaptive&quot;)"/>                                      | <img src="blue_x.png"  title="multivPenal(Surv(TIMEGAP,INDICREC)~cluster(PATIENT)+v1+v2+event2(INDICMETA)+terminal(INDICDEATH),formula.Event2=~v1+v2+v3,formula.terminalEvent=~v1,data=dataMultiv,n.knots=c(8,8,8),kappa=c(1,1,1),initialize=FALSE)"/>                       |  <img src="blue_x.png"  title="#-- Surrogacy evaluation based on ganerated data with a combination of Monte Carlo and classical Gaussian Hermite integration :
jointSurroPenal(data=data.sim,int.method=2,nb.mc=300,nb.gh=20)"/>                                     |
| Left-truncation                                 | <img src="blue_x.png"  title="frailtyPenal(formula=Surv(t1,t2,event)~cov1+cov2,data=dataNested,recurrentAG=FALSE,cross.validation=TRUE,n.knots=10,kappa=10000)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event) ~ cluster(group), data = dataNested, n.knots = 10, kappa = 10000, cross.validation = TRUE, recurrentAG = FALSE)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+cov2,data=dataNested,n.knots=8,kappa=50000)"/>                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |                                       |
| Interval Censoring                              | <img src="blue_x.png"  title="frailtyPenal(SurvIC(left,right,event) ~ treatment,n.knots=8,kappa=10000,data=bcos)"/>                       | <img src="blue_x.png"  title="frailtyPenal(SurvIC(left,right,event) ~ cluster(group)+treatment,n.knots=8,kappa=10000,data=bcos)"/>                       |                                       |                                       |  ![](orange_x.png)                     |  ![](orange_x.png)                     |                                       |                                      |                                            |                                       |                                       |                                       |                                       |
| Two strata                                      | <img src="blue_x.png"  title="frailtyPenal(Surv(time,event) ~ dukes+strata(sex),n.knots=10,kappa=c(10000,10000),data=readmission)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+dukes+strata(sex),n.knots=10,kappa=c(10000,10000),data=readmission)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+strata(cov2),data=dataNested,n.knots=8,kappa=c(50000,50000))"/>                       | <img src="blue_x.png"  title="additivePenal(Surv(t1,t2,event)~cluster(group)+var1+strata(var2)+slope(var1),cross.validation=TRUE,correlation=FALSE,data=dataAdditive,n.knots=10,kappa=c(1,1))"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+dukes+charlson+strata(sex)+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission,n.knots=8,kappa=c(2.11e+08,2.11e+08,9.53e+11))"/>                                     |                                        |                                       |                                      |                                            |                                       |                                       |                                       |                                       |
| More strata (max=6)                             | <img src="blue_x.png"  title=""/>                       | <img src="blue_x.png"  title=""/>                       |                                       |                                       |  <img src="blue_x.png"  title=""/>                       |                                        |                                       |                                      |                                            |                                       |                                       |                                       |
| Time-dependant covariates                       | <img src="blue_x.png"  title="">                       | <img src="blue_x.png"  title="">                       |                                       |                                       |  <img src="blue_x.png"  title=""/>                       |  <img src="blue_x.png"  title=""/>                       |                                       |                                      |                                            |                                       |                                       |                                       |                                       |
| Calendar timescale                              | <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~sex+dukes+charlson,data=readmission,n.knots=6,kappa=1e5,recurrentAG=TRUE)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~cluster(id)+sex+dukes+charlson,data=readmission,n.knots=6,kappa=1e5,recurrentAG=TRUE)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+cov2,data=dataNested,n.knots=8,kappa=50000)"/>                       |                                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~cluster(id)+sex+dukes+charlson+terminal(death),formula.terminalEvent=~sex+dukes+charlson,data=readmission,n.knots=10,kappa=c(9.55e9,1.41e12),recurrentAG=TRUE)"/>                       |                                        |                                       | <img src="blue_x.png"  title="frailtyPenal(formula=Surv(t.start,t.stop,event)~subcluster(id)+cluster(group)+dukes+terminal(death),formula.terminalEvent=~dukes,data=readmissionNested,recurrentAG=TRUE,n.knots=8,kappa=c(9.55e+9,1.41e+12),initialize=TRUE)"/>                      |                                            | <img src="blue_x.png"  title="trivPenal(Surv(time0,time1,new.lesions)~cluster(id)+age+treatment+who.PS+terminal(state),formula.terminalEvent=~age+treatment+who.PS+prev.resection,tumor.size~year*treatment+age+who.PS,data=colorectal,data.Longi=colorectalLongi,random=c(&quot;1&quot;,&quot;year&quot;),id=&quot;id&quot;,link=&quot;Random-effects&quot;,left.censoring=-3.33,recurrentAG=TRUE,n.knots=6,kappa=c(0.01,2),method.GH=&quot;Standard&quot;,n.nodes=7)"/>                       |  <img src="blue_x.png"  title="trivPenalNL(Surv(time0,time1,new.lesions)~cluster(id)+age+treatment+terminal(state),formula.terminalEvent=~age+treatment,biomarker=&quot;tumor.size&quot;,formula.KG~1,formula.KD~treatment,dose=&quot;dose&quot;,time.biomarker=&quot;year&quot;,data=colorectal,data.Longi=colorectalLongi,random=&quot;KG&quot;,id=&quot;id&quot;,recurrentAG=TRUE,n.knots=5,kappa=c(0.01,2),method.GH=&quot;Pseudo-adaptive&quot;)"/>                                     | <img src="blue_x.png"  title="multivPenal(Surv(TIME0,TIME1,INDICREC)~cluster(PATIENT)+v1+v2+event2(INDICMETA)+terminal(INDICDEATH),formula.Event2=~v1+v2+v3,formula.terminalEvent=~v1,data=dataMultiv,n.knots=c(8,8,8,kappa=c(1,1,1),initialize=FALSE)"/>                       | <img src="blue_x.png"  title="Text"/>                                      |    
| Weibull                                         | <img src="blue_x.png" title="frailtyPenal(Surv(time,status)~sex+age,data=kidney,hazard=&quot;Weibull&quot;)"/>                       | <img src="blue_x.png" title="frailtyPenal(Surv(time,status) ~ cluster(id)+sex+age,data=kidney,hazard=&quot;Weibull&quot;)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+cov2,data=dataNested,hazard=&quot;Weibull&quot;)"/>                       | <img src="blue_x.png"  title="additivePenal(Surv(t1,t2,event)~cluster(group)+var1+var2+slope(var1),correlation=FALSE,data=dataAdditive,hazard=&quot;Weibull&quot;)"/>                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+dukes+charlson+sex+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission,hazard=&quot;Weibull&quot;)"/>                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(group)+num.id(id)+dukes+charlson+sex+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission2,recurrentAG=FALSE,hazard=&quot;Weibull&quot;)"/>                       |                                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~subcluster(id)+cluster(group)+dukes+terminal(death),formula.terminalEvent=~dukes,hazard=('Weibull'),data=readmissionNested,recurrentAG=TRUE,initialize=FALSE)"/>                      | <img src="blue_x.png"  title="longiPenal(Surv(time1,state)~age+treatment+who.PS+prev.resection,tumor.size~year*treatment+age+who.PS,colorectalSurv,data.Longi=colorectalLongi,random=c(&quot;1&quot;,&quot;year&quot;),id=&quot;id&quot;,link=&quot;Current-level&quot;,left.censoring=-3.33,hazard=&quot;Weibull&quot;)"/>                            | <img src="blue_x.png"  title="trivPenal(Surv(gap.time,new.lesions)~cluster(id)+age+treatment+who.PS+prev.resection+terminal(state),formula.terminalEvent=~age+treatment+who.PS+prev.resection,tumor.size~year*treatment+age+who.PS,data=colorectal,data.Longi=colorectalLongi,random=c(&quot;1&quot;,&quot;year&quot;),id=&quot;id&quot;,link=&quot;Random-effects&quot;,left.censoring=-3.33,recurrentAG=FALSE,hazard=&quot;Weibull&quot;,method.GH=&quot;Pseudo-adaptive&quot;,n.nodes=7)"/>                       |  <img src="blue_x.png"  title="trivPenalNL(Surv(time0,time1,new.lesions)~cluster(id)+age+treatment+terminal(state),formula.terminalEvent=~age+treatment,biomarker=&quot;tumor.size&quot;,formula.KG~1,formula.KD~treatment,dose=&quot;dose&quot;,time.biomarker=&quot;year&quot;,data=colorectal,data.Longi=colorectalLongi,random=&quot;KG&quot;,id=&quot;id&quot;,recurrentAG=TRUE,n.knots=5,kappa=c(0.01,2),method.GH=&quot;Pseudo-adaptive&quot;,hazard=&quot;Weibull&quot;)"/>                                     | <img src="blue_x.png"  title="multivPenal(Surv(TIMEGAP,INDICREC)~cluster(PATIENT)+v1+v2+event2(INDICMETA)+terminal(INDICDEATH),formula.Event2=~v1+v2+v3,formula.terminalEvent=~v1,data=dataMultiv,hazard="Weibull")"/>                       |                                       |
| Piecewise                                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~sex+dukes+charlson,data=readmission,hazard=&quot;Piecewise-equi&quot;,nb.int=6)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t.start,t.stop,event)~cluster(id)+sex+dukes+charlson,data=readmission,hazard=&quot;Piecewise-per&quot;,nb.int=6)"/>                       | <img src="blue_x.png"  title="frailtyPenal(Surv(t1,t2,event)~cluster(group)+subcluster(subgroup)+cov1+cov2,data=dataNested,hazard=&quot;Piecewise-per&quot;,nb.int=6)"/>                       | <img src="blue_x.png"  title="additivePenal(Surv(t1,t2,event)~cluster(group)+var1+var2+slope(var1),correlation=FALSE,data=dataAdditive,hazard=&quot;Piecewise-per&quot;,nb.int=6)"/>                       |  <img src="blue_x.png"  title="frailtyPenal(Surv(time,event)~cluster(id)+dukes+charlson+sex+chemo+terminal(death),formula.terminalEvent=~dukes+charlson+sex+chemo,data=readmission,hazard=&quot;Piecewise-equi&quot;,nb.int=c(2,4))"/>                       |  <img src="blue_x.png"  title="Text "/>                       |                                       |                                      |                                            |                                       |                                       | <img src="blue_x.png"  title="multivPenal(Surv(TIMEGAP,INDICREC)~cluster(PATIENT)+v1+v2+event2(INDICMETA)+terminal(INDICDEATH),formula.Event2=~v1+v2+v3,formula.terminalEvent=~v1,data=dataMultiv,hazard="Piecewise-per",nb.int=c(6,6,6)) "/>                       |                                       |
|     `Available output`                          |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |
| Predicted frailties                             |                                       | <img src="blue_x.png"  title="fit$frailty.pred"/>                       | <img src="blue_x.png"  title="fit$frailty.pred.group
fit$frailty.pred.subgroup"/>                       | <img src="blue_x.png"  title="fit$frailty.pred
fit$frailty.pred2"/>                       |  <img src="blue_x.png"  title="fit$frailty.pred"/>                       |                                        |                                       | <img src="blue_x.png"  title="fit$frailty.pred
fit$frailty.fam.pred"/>                      | <img src="blue_x.png"  title=""/>                            | <img src="blue_x.png"  title="fit$frailty.pred"/>                       |                                       |                                       |                                       |        
| Variances of the frailties                      |                                       | <img src="blue_x.png"  title="fit$frailty.var"/>                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |  <img src="blue_x.png"  title="Text "/>                                     |
| Martingale residuals                            | <img src="blue_x.png"  title="fit$martingaleCox"/>                       | <img src="blue_x.png"  title="fit$martingale.res"/>                       | <img src="blue_x.png"  title="fit$martingale.res"/>                       | <img src="blue_x.png"  title="fit$martingale.res "/>                       |  <img src="blue_x.png"  title="fit$martingale.res
fit$martingaledeath.res"/>                       |                                        |                                       | <img src="blue_x.png"  title="fit$martingale.res
fit$martingaledeath.res"/>                      | <img src="blue_x.png"  title="fit$martingaledeath.res "/>                            | <img src="blue_x.png"  title="fit$martingale.res
fit$martingaledeath.res"/>                       |                                       |                                       |                                       |
|     `Prediction methods `                       |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                       |                                      |                                            |                                       |                                       |                                  
| Marginal prediction of a terminal event         | <img src="blue_x.png"  title="#-- prediction of death for two patients between 100 and 100+w, with w in (50,100,...,1900) :
prediction(cox.fit,datapred,t=100,window=seq(50,1900,50))"/>                       | <img src="blue_x.png"  title="prediction(shared.fit,datapred,t=100,window=seq(50,1900,50))"/>                       |                                       |                                       |  <img src="blue_x.png"  title="#-- prediction of death between 100 and 100+w given relapses (with confidence intervals) :
prediction(joint.fit,datapred,t=100,window=seq(50,1500,50),event=&quot;Terminal&quot;,MC.sample=100)"/>                       |                                        |                                       | <img src="blue_x.png"  title="#-- Compute prediction over the individuals 274 and 4 :
prediction(JointNested.fit,data=DataPred,t=500,window=seq(100,1500,200),conditional=FALSE,individual=c(274,4))"/>                      | <img src="blue_x.png"  title="#-- prediction of death between 1 year and 1+2 given history of the biomarker :
prediction(JointLongi.fit,datapredj,datapredj_longi,t=1,window=2)"/>                            | <img src="blue_x.png"  title="#-- prediction of death between 1 year and 1+w :
prediction(trivPenal.fit,datapredj,datapredj_longi,t=1,window=seq(0.5,2.5,0.2),MC.sample=100)"/>                       | <img src="blue_x.png" title="#-- prediction of death between t and t+0.5 :
prediction(trivPenalNL.fit,datapredj,datapredj_longi,t=seq(2,3,0.2),window=0.5,MC.sample=100)"/>                                       |                                       |                                       |                                       
| Conditional prediction of a terminal event      | <img src="blue_x.png"  title=""/>                       | <img src="blue_x.png"  title="prediction(shared.fit,datapred,t=100,window=seq(50,1900,50),conditional=TRUE)"/>                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |                                       |                                       
| Marginal prediction of a new recurrent event    | 			                          | <img src="blue_x.png"  title="prediction(shared.fit,datapred,t=200,window=seq(50,1900,50),event=&quot;Recurrent&quot;,MC.sample=100)"/>                       |                                       |                                       |  <img src="blue_x.png"  title="#-- prediction of death between 100 and 100+w given relapses (with confidence intervals) :
prediction(joint.fit,datapred,t=100,window=seq(50,1500,50),event=&quot;Recurrent&quot;,MC.sample=100)"/>                       |                                        |                                       |                                      | 			                                |                                       | 		                                |                                       |                                       |                                       
| Conditional prediction of a new recurrent event | 			                          | <img src="blue_x.png"  title="prediction(shared.fit,datapred,t=200,window=seq(50,1900,50),event=&quot;Recurrent&quot;,conditional=TRUE,MC.sample=100)"/>                       |                                       |                                       |                                        |                                        |                                       |                                       |                                      | 			                                | 		                                |                                       |                                       |                                       
|     `Model evaluation `                         |                                       |                                       |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                  
| Cmeasures                                       | <img src="blue_x.png"  title="Cmeasures(fit)"/>                       |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |
| Epoce                                           |                                       |                                       |                                       |                                       |  <img src="blue_x.png"  title="epoce(fit,time=c(1,1.5, 2, 2.5))"/>                       |                                        |                                       |                                      | <img src="blue_x.png"  title="epoce(fit,time=c(1,1.5, 2, 2.5))"/>                            | <img src="blue_x.png"  title="epoce(fit,time=c(1,1.5, 2, 2.5))"/>                       |                                       |                                       |                                       |
|     `Model structure `                         |                                       |                                       |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |
| STE                      |                                       |                       |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |  ![](blue_x.png)                       |
| Prediction of treatment effects                      |                                       |                        |                                       |                                       |                                        |                                        |                                       |                                      |                                            |                                       |                                       |                                       |  ![](blue_x.png)                       |
| Statistical model                               |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |<img src="blue_x.png">                       |                      |  <img src="blue_x.png">                      |<img src="blue_x.png">                       |
| Mechanistic model (ODE)                              |                                       |                                       |                                       |                                       |                                       |                                       |                                        |                                        |                                       |                                      | <img src="blue_x.png">                                           |                                      |                                       |
Table 1: Package characteristics. Blue cross is for option available for a given type of model in the package on CRAN, orange cross is for option included in the package but not on CRAN yet. Empty cells mean that an option is not available for a given type of model (either not coded yet or simply not applicable). RE = Recurrent Event; TE = Terminal Event; LO = Longitudinal Outcome; STE = Surrogate threshold effect; ODE = Ordinary Differential Equation.

#### Reference
Agnieszka Krol, Audrey Mauguen, Yassin Mazroui, Alexandre Laurent, Stefan Michiels, Virginie Rondeau (2017), [Tutorial in Joint Modeling and Prediction: A Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event.](https://www.jstatsoft.org/article/view/v081i03)
*Journal of Statistical Software*, **81**.

Casimir Ledoux Sofeu and Virginie Rondeau (2020). [How to use frailtypack for validating failure-time surrogate endpoints using individual patient data from meta-analyses of randomized controlled trials.] (https://doi.org/10.1371/journal.pone.0228098) *PLOS ONE*, **15**, 1-25.


#### Shiny
A shiny application of frailtypack is available, allowing modelisation and prediction of several models presents in frailtypack, at https://frailtypack-pkg.shinyapps.io/shiny_frailtypack.
This application can be run in a local mode in R thanks to the function runShiny().

##### Note 
If you want to take in charge frailtypack as a project, but work on a macOSX with a version of R >= 3.6, a problem must happen between openmp and clang. clang does not seem to support option '-fopenmp'.
To avoid this, you should edit your personal Makevars (~/.R/Makevars), admitting you use gfortran, by relocating 
CC=clang by CC=gfortran

