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diff --git a/DESCRIPTION b/DESCRIPTION
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+Package: EventPredInCure
+Title: Event Prediction Including Cured Population
+Version: 1.0
+Date: 2023-12-19
+Authors@R: 
+    c(person("Bo", "Wei", , "bowei1990@gmail.com", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-4430-2882")),
+            person("Kaifeng", "Lu", , "kaifenglu@gmail.com", role = c("aut"),
+           comment = c(ORCID = "0000-0002-6160-7119")),
+           person("Brent", "McHenry", , "brent.mchenry@beigene.com", role = c("aut")))
+Description: Predicts enrollment and events assumed enrollment and treatment-specific time-to-event models, and calculates test statistics for time-to-event data with cured population based on the simulation.Methods for prediction event in the existence of cured population are as described in : Chen, Tai-Tsang(2016) <doi:10.1186/s12874-016-0117-3>. 
+License: GPL (>= 2)
+Encoding: UTF-8
+RoxygenNote: 7.2.3
+Imports: perm (>= 1.0.0.2), msm (>= 1.7.0), MLEcens (>= 0.1.7), KMsurv
+        (>= 0.1.5), dplyr (>= 1.1.0), rlang (>= 1.0.6), plotly (>=
+        4.10.1), survival (>= 2.41-3), splines (>= 3.5.0), Matrix (>=
+        1.2-14), mvtnorm (>= 1.1-3), rstpm2 (>= 1.6.1), numDeriv (>=
+        2016.8-1.1), tmvtnsim (>= 0.1.3), erify (>= 0.4.0), stats (>=
+        3.5.0), utils (>= 4.1.2), lubridate (>= 1.9.2), flexsurv (>=
+        2.2.2), MASS (>= 7.3.54)
+Depends: R (>= 3.5.0)
+LazyData: true
+NeedsCompilation: no
+Packaged: 2024-01-11 21:53:14 UTC; bo1.wei@beigenecorp.net
+Author: Bo Wei [aut, cre] (<https://orcid.org/0000-0002-4430-2882>),
+  Kaifeng Lu [aut] (<https://orcid.org/0000-0002-6160-7119>),
+  Brent McHenry [aut]
+Maintainer: Bo Wei <bowei1990@gmail.com>
+Repository: CRAN
+Date/Publication: 2024-01-12 10:00:05 UTC
diff --git a/MD5 b/MD5
@@ -0,0 +1,63 @@
+c77dcfc0a1bd659dc98d2f05c717c463 *DESCRIPTION
+31b0d6c6c517e64082981d6f8946d3b2 *NAMESPACE
+6acd3d7c2933bf5b429f9d2ce41e5a74 *R/Chen_2016_event_time.R
+bda73999aec27f1fad418c104ddcab30 *R/Chen_2016_event_time_abovetime0.R
+e64b4b85e89df8865534873467fceedd *R/Chen_2016_event_time_piecewise_exp_abovetime0.R
+8b9644f21356989aa7fc667a7e1ccd39 *R/EventPredInCure-package.R
+8d5d9398313b95da524a0823f351f7ec *R/FH_test.R
+932bc6d6c300226d1c5185c1bed69bc9 *R/SP_Chen_exponential.R
+ddac2d52e445825635ed20aedd6e1b0e *R/SP_Chen_log_logistic.R
+866472a0afbc0ef6fbc502b18eb3c99b *R/SP_Chen_log_normal.R
+47d6c9ec4b66af996683c09b54b9d692 *R/SP_Chen_piecewise_exponential.R
+7c44b80b966b41f1977ade86011a2ac7 *R/SP_Chen_weibull.R
+1cb9e8c93d7ec6c878a5a77151a9ae74 *R/b4pst.R
+e590cab346562c5c2339d11790bfb300 *R/data.R
+cc252ef67d68e74fee526a323029ab4d *R/fitDropout.R
+d3cea18e4ebf333c5c4c7f95a67e1c31 *R/fitEnrollment.R
+1c9a9e284e46ae5d00f40204f8ff2f3f *R/fitEvent.R
+59ac7e63776e022bb03cd7756fef8d7e *R/getPrediction.R
+fe7b938475479f047cd64eca6152ac89 *R/loglik_Chen_exponential.R
+2aa10c61b869864d183abbaf3115287a *R/loglik_Chen_log_logistic.R
+3455362faf308b45b708bc530d162b0b *R/loglik_Chen_log_normal.R
+b77df62f4aa9df69fbcf50015ae25479 *R/loglik_Chen_piecewise_exponential.R
+3a745f5f3bb7b0393bb25aee5ee7a12f *R/loglik_Chen_weibull.R
+88b7aa41422f1587ed04993ca731ca2a *R/predictEnrollment.R
+dfd594cd22f4e223f6ae92f0cb078e2b *R/predictEvent.R
+3e84b82654198df6042ae864ec59aa04 *R/smed.R
+201b321d9fb40df5108cf48d08c22b79 *R/summarizeObserved.R
+44e0b552a28ee6534c295b3be8e7c9f2 *R/test_plot.R
+90101bdc447f005ddf1b42302fd2d96d *R/test_procedure.R
+7af23bdd28c417e82d903de2a12fbdd5 *R/tte.R
+4c9acf70a69934f0cf80ffcc01c8162b *data/finalData.rda
+d1d8f7e6018867873ec9b19910c19758 *data/interimData1.rda
+6fcceffb1322ded2359b32bda1147c03 *data/interimData2.rda
+cb53f817f25558dc00303d14b77d29db *man/Chen_2016_event_time.Rd
+646de7df21ca1403c71f5c6421d596ff *man/Chen_2016_event_time_abovetime0.Rd
+6adef9bb2064203552578587a726187f *man/Chen_2016_event_time_piecewise_exp_abovetime0.Rd
+a46b62cd46d71bb82541a3a4ce8ea2ff *man/EventPredInCure-package.Rd
+c8fc31196be7e3ddced342223bbe96d6 *man/FH_test.Rd
+36d24cdf540663cd93ba3274f5130526 *man/SP_Chen_exponential.Rd
+feef7a0b7cbe1b6192b7cfe481673aba *man/SP_Chen_log_logistic.Rd
+39d8a0046b2e04b364bdb6bd8af1fb2d *man/SP_Chen_log_normal.Rd
+5da9bbf764960a6f4f26486a98eebcf4 *man/SP_Chen_piecewise_exponential.Rd
+90e636d65af9b7fed7d811aeaa463e09 *man/SP_Chen_weibull.Rd
+743a396b942b85a210872bb43019282e *man/b4pst.Rd
+6466d7cfb2fda493f8eea7832a84a783 *man/finalData.Rd
+61f458a925ebc393051784111d6d0b0b *man/fitDropout.Rd
+7dfd71fdea5fe7829398ee802d6196cf *man/fitEnrollment.Rd
+b2df3acbc437701eee5dfb80941222f8 *man/fitEvent.Rd
+b0ae032b80b1658fe3c417b577dece08 *man/getPrediction.Rd
+9de229720633e784f155bb7a37c7c155 *man/interimData1.Rd
+ef237d5c0fc12c4942464243365df0be *man/interimData2.Rd
+44a781c67c812c653ad41fc405e69704 *man/loglik_Chen_exponential.Rd
+220ed0f2739c13dfb427a679eb80ce87 *man/loglik_Chen_log_logistic.Rd
+d1e6dc96d080a35590fbb7ef5bacb558 *man/loglik_Chen_log_normal.Rd
+20ff2347c7883aed08ee168461d792ab *man/loglik_Chen_piecewise_exponential.Rd
+97783b4baf3ee88aa2ff58d87ee206e3 *man/loglik_Chen_weibull.Rd
+d79050f1ed3bb3d154d612810024f518 *man/predictEnrollment.Rd
+8d5b426e9d5dc5e9d13792f396982e8d *man/predictEvent.Rd
+dede2b8e608b0f64dc13e9aaf92451fd *man/smed.Rd
+3552f0f10515b0635090764d49774430 *man/summarizeObserved.Rd
+11491c90b376a4b0cd6c59cceaf85886 *man/test_plot.Rd
+8d22d4491a7bb17b160a06723ff41201 *man/test_procedure.Rd
+3190926402321cc543f63900a29e614b *man/tte.Rd
diff --git a/NAMESPACE b/NAMESPACE
@@ -0,0 +1,93 @@
+# Generated by roxygen2: do not edit by hand
+
+export(Chen_2016_event_time)
+export(Chen_2016_event_time_abovetime0)
+export(Chen_2016_event_time_piecewise_exp_abovetime0)
+export(FH_test)
+export(SP_Chen_exponential)
+export(SP_Chen_log_logistic)
+export(SP_Chen_log_normal)
+export(SP_Chen_piecewise_exponential)
+export(SP_Chen_weibull)
+export(b4pst)
+export(fitDropout)
+export(fitEnrollment)
+export(fitEvent)
+export(getPrediction)
+export(loglik_Chen_exponential)
+export(loglik_Chen_log_logistic)
+export(loglik_Chen_log_normal)
+export(loglik_Chen_piecewise_exponential)
+export(loglik_Chen_weibull)
+export(predictEnrollment)
+export(predictEvent)
+export(smed)
+export(summarizeObserved)
+export(test_plot)
+export(test_procedure)
+export(tte)
+importFrom(Matrix,bdiag)
+importFrom(dplyr,"%>%")
+importFrom(dplyr,arrange)
+importFrom(dplyr,as_tibble)
+importFrom(dplyr,bind_rows)
+importFrom(dplyr,cross_join)
+importFrom(dplyr,filter)
+importFrom(dplyr,group_by)
+importFrom(dplyr,mutate)
+importFrom(dplyr,n)
+importFrom(dplyr,rename)
+importFrom(dplyr,rename_all)
+importFrom(dplyr,row_number)
+importFrom(dplyr,select)
+importFrom(dplyr,slice)
+importFrom(dplyr,summarize)
+importFrom(dplyr,tibble)
+importFrom(erify,check_bool)
+importFrom(erify,check_class)
+importFrom(erify,check_content)
+importFrom(erify,check_n)
+importFrom(erify,check_positive)
+importFrom(flexsurv,dllogis)
+importFrom(flexsurv,flexsurvspline)
+importFrom(flexsurv,pllogis)
+importFrom(flexsurv,psurvspline)
+importFrom(flexsurv,qsurvspline)
+importFrom(flexsurv,rsurvspline)
+importFrom(mvtnorm,rmvnorm)
+importFrom(numDeriv,grad)
+importFrom(plotly,add_lines)
+importFrom(plotly,add_ribbons)
+importFrom(plotly,hide_legend)
+importFrom(plotly,layout)
+importFrom(plotly,plot_ly)
+importFrom(rlang,.data)
+importFrom(rstpm2,vuniroot)
+importFrom(splines,bs)
+importFrom(stats,dlnorm)
+importFrom(stats,dweibull)
+importFrom(stats,loess.smooth)
+importFrom(stats,optim)
+importFrom(stats,optimHess)
+importFrom(stats,pexp)
+importFrom(stats,plnorm)
+importFrom(stats,plogis)
+importFrom(stats,pnorm)
+importFrom(stats,pweibull)
+importFrom(stats,qlogis)
+importFrom(stats,qnorm)
+importFrom(stats,quantile)
+importFrom(stats,rbinom)
+importFrom(stats,reorder)
+importFrom(stats,rexp)
+importFrom(stats,rlnorm)
+importFrom(stats,rmultinom)
+importFrom(stats,rnorm)
+importFrom(stats,runif)
+importFrom(stats,rweibull)
+importFrom(stats,uniroot)
+importFrom(stats,var)
+importFrom(survival,Surv)
+importFrom(survival,survfit)
+importFrom(survival,survreg)
+importFrom(tmvtnsim,rtnorm)
diff --git a/R/Chen_2016_event_time.R b/R/Chen_2016_event_time.R
@@ -0,0 +1,136 @@
+#' @title Function to generate event time in the existence of cured population
+#'
+#' @description generate event time under the delay-treatment effect and cured population setting
+#' @param u a scalar with between 0 and 1
+#' @param hr hazard ratio if this subject in the corresponding arm vs control arm
+#' @param distribution the distribution for the control arm, valid values of inputs include: exponential, weibull, log-normal, log-logistic
+#' @param p the proportion of cured population in the control arm
+#' @param lag delayed treatment effect time after treatment initialization, when the hr between treatment arm and control =1 until lag time
+#' @param a the shape parameter in the Weibull or the log-logistic distribution
+#' @param b the scale parameter in the exponential, Weibull or the log-logistic distribution
+#' @param mu the mean in the log-normal distribution
+#' @param sd the standard deviation in the log-normal distribution
+#' @return the event time
+#'
+#' @references 
+#' \itemize{
+#' \item Chen, Tai-Tsang. "Predicting analysis times in randomized clinical trials with cancer immunotherapy." 
+#' BMC medical research methodology 16.1 (2016): 1-10.
+#' }
+#' 
+#' @export
+#'
+Chen_2016_event_time<-function(u,hr,distribution,p,lag,a, b,mu,sd){
+  distribution=tolower(distribution)
+
+  if((distribution!="weibull")&(distribution!="log-normal")&(distribution!="log-logistic")&(distribution!="exponential")){
+    stop("The input of distribution is invalid. The valid input of distribution can be chosen from the following distributions: 'exponential','weibull', 'log-normal', and 'Log-logistic'.")
+  }
+  if(!is.numeric(u)|length(u)!=1|u<0|u>1){
+    stop('The input of u is invalid. The u should be a scalar between 0 and 1.')
+  }
+
+  if(!is.numeric(hr)|length(hr)!=1|hr<=0){
+    stop('The input of hr is invalid. The hr should be a positive scalar.')
+  }
+
+  if(!is.numeric(p)|length(p)!=1|p<0|p>1){
+    stop('The input of p is invalid. The valid value of p should be a scalar between 0 and 1.')
+  }
+  if(!is.numeric(lag)|length(lag)!=1|lag<0){
+    stop('The input of lag is invalid. The valid value of lag should be a a non-negative scalar.')
+  }
+  if((distribution=="weibull")|(distribution=="log-logistic")){
+    if(!is.numeric(a)|length(a)!=1|a<=0){
+      stop('The input of a is invalid. The shape paramtere a should be a positive scalar.')
+    }
+    if(!is.numeric(b)|length(b)!=1|b<=0){
+      stop('The input of b is invalid. The scale paramtere b should be a positive scalar.')
+    }
+ }
+  if(distribution=="log-normal"){
+    if(!is.numeric(mu)|length(mu)!=1){
+      stop('The input of a is invalid. The shape paramtere a should be a numerical scalar.')
+    }
+    if(!is.numeric(sd)|length(sd)!=1|sd<=0){
+      stop('The input of sd is invalid. The scale paramtere sd should be a positive scalar.')
+    }
+  }
+
+
+  if(distribution=="exponential"){
+    if(!is.numeric(b)|length(b)!=1|b<=0){
+      stop('The input of b is invalid. The scale paramtere b should be a positive scalar.')
+    }
+  }
+
+  if(distribution=='exponential'){
+    st0=p+(1-p)*exp(-(lag/b))
+    phi0=p/st0
+    if(u>=st0){
+      t=(-log((u-p)/(1-p)))*b
+    }
+    if((u/st0)^(1/hr)<=phi0){
+      t=10^10
+    }
+
+    if(u< st0&(u/st0)^(1/hr)>phi0){
+      t=(-log(((u/st0)^(1/hr)-phi0)/(1-phi0))+(lag/b))*b
+    }
+  }
+
+
+
+
+  if(distribution=='weibull'){
+    st0=p+(1-p)*exp(-(lag/b)^a)
+    phi0=p/st0
+    if(u>=st0){
+      t=(-log((u-p)/(1-p)))^(1/a)*b
+    }
+    if((u/st0)^(1/hr)<=phi0){
+      t=10^10
+    }
+
+    if(u< st0&(u/st0)^(1/hr)>phi0){
+      t=(-log(((u/st0)^(1/hr)-phi0)/(1-phi0))+(lag/b)^a)^(1/a)*b
+    }
+  }
+
+  if(distribution=='log-normal'){
+    st0=p+(1-p)*(1-stats::pnorm((log(lag)-mu)/sd))
+    phi0=p/st0
+    if(u>=st0){
+      t=exp(stats::qnorm(1-(u-p)/(1-p))*sd+mu)
+    }
+    if((u/st0)^(1/hr)<=phi0){
+      t=10^10
+    }
+
+    if(u< st0&(u/st0)^(1/hr)>phi0){
+      t=exp(stats::qnorm(1-((u/st0)^(1/hr)-phi0)/(1-phi0)*(1-stats::pnorm((log(lag)-mu)/sd)))*sd+mu)
+    }
+  }
+
+  if(distribution=='log-logistic'){
+    st0=p+(1-p)*(1/(1+(lag/b)^a))
+    phi0=p/st0
+    if(u>=st0){
+      t=b*((1-u)/(u-p))^(1/a)
+    }
+    if((u/st0)^(1/hr)<=phi0){
+      t=10^10
+    }
+
+    if(u< st0&(u/st0)^(1/hr)>phi0){
+      t=b*((1+(lag/b)^a)*(((u/st0)^(1/hr)-phi0)/(1-phi0))^(-1)-1)^(1/a)
+    }
+  }
+  return(t)
+
+
+}
+
+###
+#Chen_2016_event_time(0.6,hr=1,distribution='weibull',p=0.4,lag=3,a=5,b=5)
+