==2490768== Memcheck, a memory error detector
==2490768== Copyright (C) 2002-2026, and GNU GPL'd, by Julian Seward et al.
==2490768== Using Valgrind-3.27.1 and LibVEX; rerun with -h for copyright info
==2490768== Command: /data/localhost/ripley/R/R-devel-vg/bin/exec/R --vanilla
==2490768== 

R Under development (unstable) (2026-07-27 r90310) -- "Unsuffered Consequences"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> pkgname <- "carat"
> source(file.path(R.home("share"), "R", "examples-header.R"))
> options(warn = 1)
> library('carat')
> 
> base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
> base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
> cleanEx()
> nameEx("AdjBCD")
> ### * AdjBCD
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: AdjBCD
> ### Title: Covariate-adjusted Biased Coin Design
> ### Aliases: AdjBCD
> ### Keywords: carandom Covariate-ajusted biased coin design
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> ## create a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE), 
+                  stringsAsFactors = TRUE)
> Res <- AdjBCD(df, a = 2)
> ## view the output
> Res

	Covariate-adaptive Biased Coin Design

Data:  Real 
group = A B 
Sample size = 1000 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          2          B
pat3          1          2          2          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
             0.000              1.111              2.750 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> >50; 2 <--> 0-30; 3 <--> 30-50  
3 -- jobs 
	  1 <--> teac.; 2 <--> others; 3 <--> stu.  

> 
> ## Simulated Data
> n <- 1000
> cov_num <- 3
> level_num <- c(2, 3, 5) 
> # Set pr to follow two tips:
> #(1) length of pr should be sum(level_num);
> #(2) sum of probabilities for each margin should be 1.
> pr <- c(0.4, 0.6, 0.3, 0.4, 0.3, rep(0.2, times = 5))
> # set the design parameter
> a <- 1.8
> # obtain result
> Res.sim <- AdjBCD.sim(n, cov_num, level_num, pr, a)
> 
> 
> 
> cleanEx()
> nameEx("CompPower")
> ### * CompPower
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: compPower
> ### Title: Comparison of Powers for Different Tests under Different
> ###   Randomization methods
> ### Aliases: compPower
> 
> ### ** Examples
> 
> ##settings
> set.seed(100)
> n = 1000
> cov_num = 5
> level_num = c(2,2,2,2,2)
> pr = rep(0.5,10)
> beta = c(1,4,3,2,5,5,4,3,2,1)
> di = seq(0,0.5,0.1)
> sigma = 1
> type = "linear"
> p=0.85
> Iternum = 10 #<<for demonstration,it is suggested to be around 1000
> sl = 0.05
> weight = rep(0.1,5)
> 
> #comparison of corrected t-test under StrBCD and PocSim
> ##data generation
> library("ggplot2")
> Strctp=evalPower(n,cov_num,level_num,pr,type,beta,di,
+                 sigma,Iternum,sl,"StrBCD","corr.test",FALSE,p)
> PSctp=evalPower(n,cov_num,level_num,pr,type,beta,di,sigma,
+                 Iternum,sl,"PocSimMIN","corr.test",FALSE,weight,p)
> powers = list(Strctp,PSctp)
> testname = c("StrBCD.corr","PocSimMIN.corr")
> 
> #get plot and matrix for comparison
> cp = compPower(powers,di,testname)
> cp
$powers
                        0        0.1        0.2  0.3  0.4  0.5
StrBCD.corr    0.2(0.126) 0.1(0.095) 0.9(0.095) 1(0) 1(0) 1(0)
PocSimMIN.corr       0(0) 0.4(0.155)       1(0) 1(0) 1(0) 1(0)

$plot

> 
> 
> 
> cleanEx()

detaching ‘package:ggplot2’

> nameEx("DoptBCD")
> ### * DoptBCD
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: DoptBCD
> ### Title: Atkinson's D_A-optimal Biased Coin Design
> ### Aliases: DoptBCD
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> df <- data.frame("gender" = sample(c("female", "male"), 100, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 100, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 100, TRUE), 
+                  stringsAsFactors = TRUE)
> Res <- DoptBCD(df)
> ## view the output
> Res

	Atkinson's Optimum Biased Coin Design

Data:  Real 
group = A B 
Sample size = 100 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          1          A
pat3          1          1          2          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
             2.000              2.222              2.250 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> 0-30; 2 <--> >50; 3 <--> 30-50  
3 -- jobs 
	  1 <--> others; 2 <--> stu.; 3 <--> teac.  

> 
> ## Simulated Data
> n <- 1000
> cov_num <- 2 
> 
> level_num <- c(2, 5)
> # Set pr to follow two tips:
> #(1) length of pr should be sum(level_num);
> #(2)sum of probabilities for each margin should be 1.
> pr <- c(0.4, 0.6, rep(0.2, times = 5))
> Res.sim <- DoptBCD.sim(n, cov_num, level_num, pr)
> ## view the output
> Res.sim

	Atkinson's Optimum Biased Coin Design with Two Arms

Data:  Simulated 
group = A B 
Sample size = 1000 
cov_num = 2 
level_num = 2 5 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 assignment
pat1          2          5          B
pat2          1          1          A
pat3          1          3          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
             6.000              8.800              7.143 


> 
> 
> 
> 
> cleanEx()
> nameEx("HuHuCAR")
> ### * HuHuCAR
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: HuHuCAR
> ### Title: Hu and Hu's General Covariate-Adaptive Randomization
> ### Aliases: HuHuCAR
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> ## create a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE), 
+                  stringsAsFactors = TRUE)
> omega <- c(1, 2, rep(1, 3))
> Res <- HuHuCAR(data = df, omega)
> ## view the output
> Res

	Hu and Hu's General CAR

Data:  Real 
group = A B 
Sample size = 1000 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          2          A
pat3          1          2          2          B

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
                 0                  1                  1 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> >50; 2 <--> 0-30; 3 <--> 30-50  
3 -- jobs 
	  1 <--> teac.; 2 <--> others; 3 <--> stu.  

> 
> ## Simulated data
> cov_num <- 3
> level_num <- c(2, 3, 3)
> pr <- c(0.4, 0.6, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3)
> omega <- rep(0.2, times = 5)
> Res.sim <- HuHuCAR.sim(n = 100, cov_num, level_num, pr, omega)
> ## view the output
> Res.sim

	Hu and Hu's General CAR

Data:  Simulated 
group = A B 
Sample size = 100 
cov_num = 3 
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          2          2          2          B
pat2          1          2          1          A
pat3          1          1          2          B

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
              0.00               1.00               0.75 


> 
> 
> 
> 
> cleanEx()
> nameEx("PocSimMIN")
> ### * PocSimMIN
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: PocSimMIN
> ### Title: Pocock and Simon's Method in the Two-Arms Case
> ### Aliases: PocSimMIN
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> ## creat a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE), 
+                  stringsAsFactors = TRUE)
> weight <- c(1, 2, 1)
> Res <- PocSimMIN(data = df, weight)
> ## view the output
> Res

	Pocock and Simon's Procedure with Two Arms

Data:  Real 
group = A B 
Sample size = 1000 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          2          A
pat3          1          2          2          B

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
             0.000              4.111              1.250 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> >50; 2 <--> 0-30; 3 <--> 30-50  
3 -- jobs 
	  1 <--> teac.; 2 <--> others; 3 <--> stu.  

> 
> ## Simulated Data
> cov_num = 3
> level_num = c(2, 3, 3)
> pr = c(0.4, 0.6, 0.3, 0.3, 0.4, 0.4, 0.3, 0.3)
> Res.sim <- PocSimMIN.sim(n = 1000, cov_num, level_num, pr)
> ## view the output
> Res.sim

	Pocock and Simon's Procedure with Two Arms

Data:  Simulated 
group = A B 
Sample size = 1000 
cov_num = 3 
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          2          3          2          B
pat2          1          1          3          A
pat3          1          3          2          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
             2.000              5.667              0.750 


> 
> 
> 
> 
> cleanEx()
> nameEx("StrBCD")
> ### * StrBCD
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: StrBCD
> ### Title: Shao's Method in the Two-Arms Case
> ### Aliases: StrBCD
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> ## creat a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE), 
+                  stringsAsFactors = TRUE)
> Res <- StrBCD(data = df)
> ## view the output
> Res

	Shao's Procedure

Data:  Real 
group = A B 
Sample size = 1000 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          2          B
pat3          1          2          2          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
            0.0000             0.6667             1.0000 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> >50; 2 <--> 0-30; 3 <--> 30-50  
3 -- jobs 
	  1 <--> teac.; 2 <--> others; 3 <--> stu.  

> 
> ## Simulated Data
> cov_num = 3
> level_num = c(2, 3, 3)
> pr = c(0.4, 0.6, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3)
> Res.sim <- StrBCD.sim(n = 1000, cov_num, level_num, pr)
> ## view the output
> Res.sim

	Shao's Procedure

Data:  Simulated 
group = A B 
Sample size = 1000 
cov_num = 3 
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          2          3          2          B
pat2          1          1          3          A
pat3          1          2          2          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall       within-strt. within-cov.-margin 
            0.0000             0.7778             1.7500 


> 
> 
> 
> 
> cleanEx()
> nameEx("StrPBR")
> ### * StrPBR
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: StrPBR
> ### Title: Stratified Permuted Block Randomization
> ### Aliases: StrPBR
> 
> ### ** Examples
> 
> # a simple use
> ## Real Data
> ## creat a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 100, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 100, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 100, TRUE), 
+                  stringsAsFactors = TRUE)
> Res <- StrPBR(data = df, bsize = 4)
==2490768== Invalid read of size 8
==2490768==    at 0x53A83E: STRING_ELT (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:480)
==2490768==    by 0x53A83E: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:196)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768==    by 0x1C1938B1: Rcpp_fast_eval (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/api/meat/Rcpp_eval.h:51)
==2490768==    by 0x1C1938B1: Rcpp::Function_Impl<Rcpp::PreserveStorage>::invoke(SEXPREC*, SEXPREC*) const (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:138)
==2490768==    by 0x1C148EEF: operator()<char [8], int, char [2], SEXPREC*, char [2], Rcpp::traits::named_object<char [1]> > (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:100)
==2490768==    by 0x1C148EEF: nameString(unsigned int, arma::Col<double>, int, Rcpp::String, arma::Mat<double>) (/data/localhost/ripley/R/packages/tests-vg/carat/src/InnerFun.cpp:252)
==2490768==    by 0x1C1A5615: _carat_nameString (/data/localhost/ripley/R/packages/tests-vg/carat/src/RcppExports.cpp:97)
==2490768==  Address 0x1c27cd28 is 4,984 bytes inside a block of size 7,960 alloc'd
==2490768==    at 0x4840AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447)
==2490768==    by 0x529A79: GetNewPage (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:998)
==2490768==    by 0x52BAF3: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2872)
==2490768==    by 0x53A76C: Rf_allocVector (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:609)
==2490768==    by 0x53A76C: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:174)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768== 
==2490768== Invalid read of size 8
==2490768==    at 0x53A91E: STRING_ELT (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:480)
==2490768==    by 0x53A91E: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:212)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768==    by 0x1C1938B1: Rcpp_fast_eval (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/api/meat/Rcpp_eval.h:51)
==2490768==    by 0x1C1938B1: Rcpp::Function_Impl<Rcpp::PreserveStorage>::invoke(SEXPREC*, SEXPREC*) const (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:138)
==2490768==    by 0x1C148EEF: operator()<char [8], int, char [2], SEXPREC*, char [2], Rcpp::traits::named_object<char [1]> > (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:100)
==2490768==    by 0x1C148EEF: nameString(unsigned int, arma::Col<double>, int, Rcpp::String, arma::Mat<double>) (/data/localhost/ripley/R/packages/tests-vg/carat/src/InnerFun.cpp:252)
==2490768==    by 0x1C1A5615: _carat_nameString (/data/localhost/ripley/R/packages/tests-vg/carat/src/RcppExports.cpp:97)
==2490768==  Address 0x1c27cd28 is 4,984 bytes inside a block of size 7,960 alloc'd
==2490768==    at 0x4840AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447)
==2490768==    by 0x529A79: GetNewPage (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:998)
==2490768==    by 0x52BAF3: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2872)
==2490768==    by 0x53A76C: Rf_allocVector (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:609)
==2490768==    by 0x53A76C: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:174)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768== 
==2490768== Invalid read of size 8
==2490768==    at 0x53AA47: STRING_ELT (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:480)
==2490768==    by 0x53AA47: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:232)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768==    by 0x1C1938B1: Rcpp_fast_eval (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/api/meat/Rcpp_eval.h:51)
==2490768==    by 0x1C1938B1: Rcpp::Function_Impl<Rcpp::PreserveStorage>::invoke(SEXPREC*, SEXPREC*) const (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:138)
==2490768==    by 0x1C148EEF: operator()<char [8], int, char [2], SEXPREC*, char [2], Rcpp::traits::named_object<char [1]> > (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/Function.h:100)
==2490768==    by 0x1C148EEF: nameString(unsigned int, arma::Col<double>, int, Rcpp::String, arma::Mat<double>) (/data/localhost/ripley/R/packages/tests-vg/carat/src/InnerFun.cpp:252)
==2490768==    by 0x1C1A5615: _carat_nameString (/data/localhost/ripley/R/packages/tests-vg/carat/src/RcppExports.cpp:97)
==2490768==  Address 0x1c27cd28 is 4,984 bytes inside a block of size 7,960 alloc'd
==2490768==    at 0x4840AE6: malloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:447)
==2490768==    by 0x529A79: GetNewPage (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:998)
==2490768==    by 0x52BAF3: Rf_allocVector3 (/data/localhost/ripley/R/svn/R-devel/src/main/memory.c:2872)
==2490768==    by 0x53A76C: Rf_allocVector (/data/localhost/ripley/R/svn/R-devel/src/include/Rinlinedfuns.h:609)
==2490768==    by 0x53A76C: do_paste (/data/localhost/ripley/R/svn/R-devel/src/main/paste.c:174)
==2490768==    by 0x4D92DD: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8150)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768==    by 0x4E746F: applyClosure_core (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2314)
==2490768==    by 0x4E4A38: Rf_applyClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2333)
==2490768==    by 0x4E4A38: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1278)
==2490768==    by 0x47EAE0: R_UnwindProtect (/data/localhost/ripley/R/svn/R-devel/src/main/context.c:984)
==2490768==    by 0x1C18FA04: Rcpp::unwindProtect(SEXPREC* (*)(void*), void*) (/data/localhost/ripley/R/test-dev/Rcpp/include/Rcpp/unwindProtect.h:68)
==2490768== 
> ## view the output
> Res

	Stratified Permuted Block Randomization

Data:  Real 
group = A B 
block = 4 
Sample size = 100 
cov_num = 3 
considered covariates:   gender  age  jobs  
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          1          1          1          B
pat2          1          1          1          A
pat3          1          1          2          B

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall within-cov.-margin           pnum = 1           pnum = 2 
             2.000              1.500              0.250              1.167 
          pnum = 3           pnum = 4 
             0.250              0.250 

Remark-Index: 
1 -- gender 
	  1 <--> male; 2 <--> female  
2 -- age 
	  1 <--> 0-30; 2 <--> >50; 3 <--> 30-50  
3 -- jobs 
	  1 <--> others; 2 <--> stu.; 3 <--> teac.  

> 
> ## Simulated data
> cov_num <- 3
> level_num <- c(2, 3, 3)
> pr <- c(0.4, 0.6, 0.3, 0.4, 0.3, 0.4, 0.3, 0.3)
> Res.sim <- StrPBR.sim(n = 100, cov_num, level_num, pr)
> ## view the output
> Res.sim

	Stratified Permuted Block Randomization

Data:  Simulated 
group = A B 
block = 4 
Sample size = 100 
cov_num = 3 
level_num = 2 3 3 

the first three patients' covariate-profiles and assignments:
     covariate1 covariate2 covariate3 assignment
pat1          2          2          3          B
pat2          1          2          2          A
pat3          2          3          3          A

Mean absolute imbalances at overall, within-strt., and within-cov.-margin levels:
           overall within-cov.-margin           pnum = 1           pnum = 2 
              0.00               1.00               0.60               0.75 
          pnum = 3           pnum = 4 
              0.20               0.75 


> 
> 
> 
> 
> cleanEx()
> nameEx("boot.test")
> ### * boot.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: boot.test
> ### Title: Bootstrap t-test
> ### Aliases: boot.test
> 
> ### ** Examples
> 
> #Suppose the data used is patients' profile from real world, 
> #  while it is generated here. Data needs to be preprocessed 
> #  and then get assignments following certain randomization.
> set.seed(100)
> df<- data.frame("gender" = sample(c("female", "male"), 100, TRUE, c(1 / 3, 2 / 3)),
+                 "age" = sample(c("0-30", "30-50", ">50"), 100, TRUE),
+                 "jobs" = sample(c("stu.", "teac.", "other"), 100, TRUE, c(0.4, 0.2, 0.4)), 
+                 stringsAsFactors = TRUE)
> ##data preprocessing
> data.pd <- StrPBR(data = df, bsize = 4)$Cov_Assig
> 
> #Then we need to combine patients' profiles and outcomes after randomization and treatments.
> outcome = runif(100)
> data.combined = data.frame(rbind(data.pd,outcome), stringsAsFactors = TRUE)
> 
> #run the bootstrap t-test
> B = 200
> Strbt = boot.test(data.combined, B, "StrPBR", bsize = 4)
> Strbt

	Bootstrap t-test

data:  data.combined
t = 0.50134, p-value = 0.6161
95 percent confidence interval:
 -0.0806597  0.1361060
sample estimates:
difference for treatment effect 
                     0.02772313 

> 
> 
> 
> cleanEx()
> nameEx("compRand")
> ### * compRand
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: compRand
> ### Title: Compare Different Randomization Procedures via Tables and Plots
> ### Aliases: compRand
> 
> ### ** Examples
> 
> ## Compare stratified permuted block randomization and Hu and Hu's general CAR
> cov_num <- 2
> level_num <- c(2, 2)
> pr <- rep(0.5, 4)
> n <- 500
> N <- 20 # <<adjust according to CPU
> bsize <- 4
> # set weight for Hu and Hu's method, it satisfies
> # (1)Length should equal to cov_num
> omega <- c(1, 2, 1, 1)
> # Assess Hu and Hu's general CAR
> Obj1 <- evalRand.sim(n = n, N = N, Replace = FALSE, cov_num = cov_num, 
+                      level_num = level_num, pr = pr, method = "HuHuCAR", 
+                      omega, p = 0.85)
> # Assess stratified permuted block randomization
> Obj2 <- evalRand.sim(n = n, N = N, Replace = FALSE, cov_num = cov_num, 
+                      level_num = level_num, pr = pr, method = "StrPBR", 
+                      bsize)
> 
> RES <- compRand(Obj1, Obj2)
> 
> 
> 
> 
> cleanEx()
> nameEx("corr.test")
> ### * corr.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: corr.test
> ### Title: Corrected t-test
> ### Aliases: corr.test
> 
> ### ** Examples
> 
> ##generate data
> set.seed(100)
> n = 1000
> cov_num = 5
> level_num = c(2,2,2,2,2)
> pr = rep(0.5,10)
> beta = c(0.1,0.4,0.3,0.2,0.5,0.5,0.4,0.3,0.2,0.1)
> omega = c(0.1, 0.1, rep(0.8 / 5, times = 5))
> mu1 = 0
> mu2 = 0.7
> sigma = 1
> type = "linear"
> p = 0.85
> 
> dataH = getData(n,cov_num,level_num,pr,type,beta,
+                 mu1,mu2,sigma,"HuHuCAR",omega,p)
> 
> #run the corrected t-test
> HHct=corr.test(dataH)
> HHct

	Corrected t-test

data:  dataH
t = -11.107, p-value < 2.2e-16
95 percent confidence interval:
 -0.8260204 -0.5782327
sample estimates:
difference for treatment effect 
                     -0.7021266 

> 
> 
> 
> cleanEx()
> nameEx("evalPower")
> ### * evalPower
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: evalPower
> ### Title: Evaluation of Tests and Randomization Procedures through Power
> ### Aliases: evalPower
> 
> ### ** Examples
> 
> ##settings
> set.seed(2019)
> n = 100#<<for demonstration,it is suggested to be larger than 1000
> cov_num = 5
> level_num = c(2,2,2,2,2)
> pr = rep(0.5,10)
> beta = c(0.1,0.4,0.3,0.2,0.5,0.5,0.4,0.3,0.2,0.1)
> omega = c(0.1, 0.1, rep(0.8 / 5, times = 5))
> di = seq(0,0.5,0.1)
> sigma = 1
> type = "linear"
> p = 0.85
> Iternum = 10#<<for demonstration,it is suggested to be around 1000
> sl = 0.05
> Reps = 10#<<for demonstration,it is suggested to be 200
> 
> #Evaluation of Power
> library("ggplot2")
> Strtp=evalPower(n,cov_num,level_num,pr,type,beta,di,sigma,
+                 Iternum,sl,"HuHuCAR","rand.test",TRUE,omega,p,Reps, nthreads = 1)
> Strtp
$Powers
  diff value    se
1  0.0   0.2 0.126
2  0.1   0.0 0.000
3  0.2   0.6 0.155
4  0.3   0.6 0.155
5  0.4   1.0 0.000
6  0.5   0.9 0.095

$Plot

$Time
[1] "Execute time: 8.69 secs"

> 
> 
> 
> cleanEx()

detaching ‘package:ggplot2’

> nameEx("evalRand")
> ### * evalRand
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: evalRand
> ### Title: Evaluation of Randomization Procedures
> ### Aliases: evalRand
> 
> ### ** Examples
> 
> # a simple use
> ## Access by real data
> ## create a dataframe
> df <- data.frame("gender" = sample(c("female", "male"), 1000, TRUE, c(1 / 3, 2 / 3)), 
+                  "age" = sample(c("0-30", "30-50", ">50"), 1000, TRUE), 
+                  "jobs" = sample(c("stu.", "teac.", "others"), 1000, TRUE), 
+                  stringsAsFactors = TRUE)
> Res <- evalRand(data = df, method = "HuHuCAR", N = 500, 
+                 omega = c(1, 2, rep(1, ncol(df))), p = 0.85)
> ## view the output
> Res

	Hu and Hu's General CAR

call:
 evalRand(method = HuHuCAR)

group = A B 
Sample size = 1000 
iteration = 500 
cov_num = 3 
level_num = 2 3 3 
Data type:  Real 

assignments of the first 3 iterations for the first 7 patients : 
      pat1 pat2 pat3 pat4 pat5 pat6 pat7
iter1    B    A    B    A    B    A    B
iter2    B    A    B    A    A    B    B
iter3    B    A    B    A    A    B    A

Evaluation by imbalances: 
absolute overall imbalances:
     max 95% quan   median     mean 
   6.000    2.000    0.000    0.812 

absolute within-strt. imbalances for the first 3 strata: 
                max 95% quan median mean
stratum1(1,1,1)   5        3      1 1.23
stratum2(1,1,2)   5        3      1 1.22
stratum3(1,1,3)   5        3      1 1.22

absolute within-cov.-margin imbalances for 3 margins: 
            max 95% quan median mean
margin(1;1)   6        4      0 1.08
margin(2;1)   7        3      1 1.38
margin(3;1)   7        3      1 1.46

Remark-Index: 
1 -- gender 
	  1 <--> male  2 <--> female  
2 -- age 
	  1 <--> >50  2 <--> 0-30  3 <--> 30-50  
3 -- jobs 
	  1 <--> teac.  2 <--> others  3 <--> stu.  
> 
> ## Assess by simulated data
> cov_num <- 3
> level_num <- c(2, 3, 5)
> pr <- c(0.35, 0.65, 0.25, 0.35, 0.4, 0.25, 0.15, 0.2, 0.15, 0.25)
> n <- 1000
> N <- 50
> omega = c(1, 2, 1, 1, 2)
> # assess Hu and Hu's procedure with the same group of patients
> Res.sim <- evalRand.sim(n = n, N = N, Replace = FALSE, cov_num = cov_num, 
+                         level_num = level_num, pr = pr, method = "HuHuCAR", 
+                         omega, p = 0.85)
> 
> 
> 
> cleanEx()
> nameEx("getData")
> ### * getData
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: getData
> ### Title: Data Generation
> ### Aliases: getData
> 
> ### ** Examples
> 
> #Parameters' Setting
> set.seed(100)
> n = 1000
> cov_num = 5
> level_num = c(2,2,2,2,2)
> beta = c(1,4,3,2,5,5,4,3,2,1)
> mu1 = 0
> mu2 = 0
> sigma = 1
> type = "linear"
> p = 0.85
> omega = c(0.1, 0.1, rep(0.8 / 5, times = 5))
> pr = rep(0.5,10)
> 
> #Data Generation
> dataH = getData(n, cov_num,level_num, pr, type, beta,
+                 mu1, mu2, sigma, "HuHuCAR", omega, p)
> dataH[1:(cov_num+2),1:5]
                X1       X2       X3       X4       X5
covariate1  1.0000  1.00000  2.00000  1.00000  1.00000
covariate2  1.0000  1.00000  2.00000  2.00000  1.00000
covariate3  2.0000  2.00000  2.00000  1.00000  2.00000
covariate4  1.0000  1.00000  2.00000  1.00000  2.00000
covariate5  2.0000  1.00000  1.00000  2.00000  1.00000
assignment  2.0000  1.00000  1.00000  2.00000  2.00000
outcome    13.9634 14.43592 16.00091 14.59801 13.28392
> 
> 
> 
> cleanEx()
> nameEx("rand.test")
> ### * rand.test
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: rand.test
> ### Title: Randomization Test
> ### Aliases: rand.test
> 
> ### ** Examples
> 
> ##generate data
> set.seed(100)
> n = 1000
> cov_num = 5
> level_num = c(2,2,2,2,2)
> pr = rep(0.5,10)
> beta = c(0.1,0.4,0.3,0.2,0.5,0.5,0.4,0.3,0.2,0.1)
> mu1 = 0
> mu2 = 0.01
> sigma = 1
> type = "linear"
> p = 0.85
> 
> dataS = getData(n, cov_num, level_num, pr, type,
+                beta, mu1, mu2, sigma, "StrBCD", p)
> 
> #run the randomization test
> library("ggplot2")
> Strt = rand.test(data = dataS, Reps = 200,method = "StrBCD", 
+                 conf = 0.95, binwidth = 30,
+                 p = 0.85)
> Strt

	Randomization test

data:  dataS
p-value = 0.005
95 percent confidence interval:
 -2.390275e-01 -4.226958e+54
sample estimates:
difference for treatment effect 
                     -0.1913866 

> 
> 
> 
> ### * <FOOTER>
> ###
> cleanEx()

detaching ‘package:ggplot2’

> options(digits = 7L)
> base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
Time elapsed:  322.978 1.357 326.014 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')
==2490768== 
==2490768== HEAP SUMMARY:
==2490768==     in use at exit: 136,467,920 bytes in 31,007 blocks
==2490768==   total heap usage: 12,312,736 allocs, 12,281,729 frees, 5,286,314,395 bytes allocated
==2490768== 
==2490768== 384 bytes in 1 blocks are possibly lost in loss record 198 of 2,775
==2490768==    at 0x484862B: calloc (/builddir/build/BUILD/valgrind-3.27.1-build/valgrind-3.27.1/coregrind/m_replacemalloc/vg_replace_malloc.c:1616)
==2490768==    by 0x40120BF: calloc (/usr/src/debug/glibc-2.43-7.fc44.x86_64/elf/../include/rtld-malloc.h:44)
==2490768==    by 0x40120BF: allocate_dtv (/usr/src/debug/glibc-2.43-7.fc44.x86_64/elf/../elf/dl-tls.c:470)
==2490768==    by 0x4012B81: _dl_allocate_tls (/usr/src/debug/glibc-2.43-7.fc44.x86_64/elf/../elf/dl-tls.c:722)
==2490768==    by 0x56CF9CE: allocate_stack (/usr/src/debug/glibc-2.43-7.fc44.x86_64/nptl/allocatestack.c:568)
==2490768==    by 0x56CF9CE: pthread_create@@GLIBC_2.34 (/usr/src/debug/glibc-2.43-7.fc44.x86_64/nptl/pthread_create.c:680)
==2490768==    by 0x11C02586: cli__start_thread (/tmp/RtmpAH1G4A/R.INSTALL6fb6d28338fd0/cli/src/thread.c:54)
==2490768==    by 0x11C025C7: clic_start_thread (/tmp/RtmpAH1G4A/R.INSTALL6fb6d28338fd0/cli/src/thread.c:78)
==2490768==    by 0x49714D: R_doDotCall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:760)
==2490768==    by 0x4976C3: do_dotcall (/data/localhost/ripley/R/svn/R-devel/src/main/dotcode.c:1437)
==2490768==    by 0x4DEE66: bcEval_loop (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:8085)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7533)
==2490768==    by 0x4E4461: bcEval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:7518)
==2490768==    by 0x4E482A: Rf_eval (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:1167)
==2490768==    by 0x4E66C6: R_execClosure (/data/localhost/ripley/R/svn/R-devel/src/main/eval.c:2398)
==2490768== 
==2490768== LEAK SUMMARY:
==2490768==    definitely lost: 0 bytes in 0 blocks
==2490768==    indirectly lost: 0 bytes in 0 blocks
==2490768==      possibly lost: 384 bytes in 1 blocks
==2490768==    still reachable: 136,467,536 bytes in 31,006 blocks
==2490768==         suppressed: 0 bytes in 0 blocks
==2490768== Reachable blocks (those to which a pointer was found) are not shown.
==2490768== To see them, rerun with: --leak-check=full --show-leak-kinds=all
==2490768== 
==2490768== For lists of detected and suppressed errors, rerun with: -s
==2490768== ERROR SUMMARY: 4 errors from 4 contexts (suppressed: 0 from 0)