This page describes the master PKPD analysis datasets used in the
majority of the plots on this site. The datasets were simulated from the
indirect response model below, using rxode2. Data
specifications can be accessed on Datasets.
One dataset was created for single ascending dose PK and another dataset
for multiple ascending dose PK and PD. Explore the datasets in the
tables below, or download from the following links:
The generation code below is shown for reference but is not run when
the site is built. The datasets it produces are committed under
Data/, and the tables on this page are rendered from those
files. See “Regenerating the datasets” at the foot of the page.
library(dplyr)
library(rxode2)ode <- "
Concentration = centr/V2;
C3 = peri/V3;
C4 = peri2/V4;
d/dt(depot) = -KA*depot;
d/dt(centr) = KA*depot - (CL+Q+Q2)*Concentration + Q*C3 + Q2*C4;
d/dt(peri) = Q*Concentration - Q*C3;
d/dt(peri2) = Q2*Concentration - Q2*C4;
d/dt(eff) = Kin*(1+Emax*Concentration/(EC50 + Concentration)) - Kout*(1)*eff;
"
work <- tempfile("Rx_intro-")
mod1 <- rxode2(model = ode, modName = "mod1")
cat(gsub(pattern="\nConcentration",replacement="Concentration",x=gsub(pattern=";\n",replacement=" \n", x = ode)))set.seed(12345666)
ndose <- 5
nsub <- 10 # number of subproblems
SEX <- rep(c(rep("Male",ceiling(nsub/2)),rep("Female",floor(nsub/2))),ndose)
WT0 <- (SEX=="Male")*runif(length(SEX),60,110)+(SEX=="Female")*runif(length(SEX),50,100)
VCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CLCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CL <- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA <- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 <- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV
DOSE <- sort(rep(c(100,200,400,800,1600),nsub))
theta.all <-
cbind(KA=KA, CL=CL, V2=V2,
Q=10*CLCOV, V3=100*VCOV, V4 = 10000*VCOV, Q2 = 10*CLCOV,
Kin=0.5, Kout=0.05, EC50 = 1, Emax = 4)
# Set nominal times for sampling schedule
SAMPLING <- c(-0.1,0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9)
DOSING <- c(0)
NT <- data.frame(NT = c(SAMPLING, DOSING), label = c(rep("SAMPLING",length(SAMPLING)),rep("DOSING",length(DOSING))))
NT <- NT[order(NT$NT),]
ev.all <- list()
for(idose in 1:length(DOSE)){
ev.all[[idose]] <- eventTable(amount.units='mg', time.units='hours')
# ev.all[[idose]]$add.dosing(dose = DOSE[idose], nbr.doses = 1, dosing.interval = 24)
# ev.all[[idose]]$add.sampling(c(c(-0.1,0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9)))
# Include some noise in the sampling & dosing schedules
temp <- NT
temp$time <- cumsum(c(temp$NT[1] + 0.1*rnorm(1), abs(temp$NT[2:length(temp$NT)]-temp$NT[1:length(temp$NT)-1] + 0.1*rnorm(length(temp$NT)-1))))
temp$time <- temp$time - temp$time[temp$NT==0] # center at first dose
ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label=="DOSING"])
ev.all[[idose]]$add.sampling(temp$time[temp$label=="SAMPLING"])
}
x.all.df <- NULL
ID = 0
for(i in 1:length(ev.all)){
# for(i in 1:nsub){
ID = ID + 1
theta <- theta.all[i,]
inits <- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta["Kin"]/theta["Kout"])
x <- mod1$solve(theta, ev.all[[i]], inits = inits)
x.df <- data.frame(x)
x.df$NT <- SAMPLING
temp <- data.frame(ev.all[[i]]$get.dosing())
temp$NT <- DOSING
temp[,setdiff(names(x.df),names(temp))]<- NA
x.df[,setdiff(names(temp),names(x.df))]<- NA
x.df <- rbind(x.df,temp)
x.df$ID <- ID
x.df$DOSE <- DOSE[i]
x.df$WT0 <- WT0[i]
x.df$SEX <- SEX[i]
if(is.null(x.all.df)){
x.all.df <- x.df
}else{
x.all.df <- rbind(x.all.df, x.df)
}
}
x.all.df$DV <- x.all.df$Concentration*(exp(rnorm(length(x.all.df$Concentration),0,0.6^2))) +
0.01*rnorm(length(x.all.df$Concentration))
x.all.df$DV[x.all.df$DV<0.05]=0.05
x.all.df$DV[x.all.df$Concentration==0]=NA
x.all.df$BLQ=0
x.all.df$BLQ[x.all.df$DV==0.05]=1
my.data <- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT <- 1
my.data$CMT_label <- "Dosing"
temp <- x.all.df[is.na(x.all.df$amt),]
temp$CMT <- 2
temp$CMT_label <- "PK Concentration"
my.data <- rbind(my.data,temp)
my.data$DV <- signif(my.data$DV,3)
my.data$WT0 <- signif(my.data$WT0,3)
my.data$time <- round(my.data$time,3)
my.data <- my.data[order(my.data$ID,my.data$time, my.data$CMT),]
my.data$DOSE_label <- paste(my.data$DOSE,"mg")
my.data$DOSE_label[my.data$DOSE==0] <- "Placebo"
my.data$DOSE_label <- factor(my.data$DOSE_label,levels = c("Placebo",paste(unique(my.data$DOSE[my.data$DOSE!=0]),"mg")))
Single_Ascending_Dose_Dataset <- my.data[,c("ID","time","NT","amt","DV","CMT","CMT_label","BLQ","evid","WT0","SEX","DOSE","DOSE_label")]
write.csv(Single_Ascending_Dose_Dataset,"../Data/Single_Ascending_Dose_Dataset.csv",row.names = FALSE)
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset
Single_Ascending_Dose_Dataset2$PROFTIME <- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$NOMTIME <- Single_Ascending_Dose_Dataset2$NT
Single_Ascending_Dose_Dataset2$YTYPE <- Single_Ascending_Dose_Dataset2$CMT
Single_Ascending_Dose_Dataset2$amt[is.na(Single_Ascending_Dose_Dataset2$amt)] <- 0
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset2[!(Single_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Single_Ascending_Dose_Dataset2$CMT==2),]
Single_Ascending_Dose_Dataset2$BLQ[Single_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Single_Ascending_Dose_Dataset2$CMT==2] <- 1L
Single_Ascending_Dose_Dataset2$evid <- plyr::mapvalues(Single_Ascending_Dose_Dataset2$evid, c(NA,101), c(0,1))
Single_Ascending_Dose_Dataset2$TIMEUNIT <- "Hours"
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==1] <- "mg"
Single_Ascending_Dose_Dataset2$EVENTU[Single_Ascending_Dose_Dataset2$CMT==2] <- "ng/mL"
Single_Ascending_Dose_Dataset2 <- Single_Ascending_Dose_Dataset2[,c("ID","time","NOMTIME","TIMEUNIT","amt","DV","CMT",
"CMT_label","EVENTU","BLQ","evid","WT0","SEX","DOSE_label",
"DOSE")]
names(Single_Ascending_Dose_Dataset2) <- c("ID","TIME","NOMTIME","TIMEUNIT","AMT","LIDV","CMT",
"NAME","EVENTU","CENS","EVID","WEIGHTB","SEX","TRTACT",
"DOSE")
write.csv(Single_Ascending_Dose_Dataset2,"../Data/Single_Ascending_Dose_Dataset2.csv",row.names = FALSE)DT::datatable(Single_Ascending_Dose_Dataset2, rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE)) set.seed(12345666)
ndose <- 6
nsub <- 10 # number of subproblems
SEX <- rep(c(rep("Male",ceiling(nsub/2)),rep("Female",floor(nsub/2))),ndose)
WT0 <- (SEX=="Male")*runif(length(SEX),60,110)+(SEX=="Female")*runif(length(SEX),50,100)
VCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CLCOV <- (WT0/70)*(1-0.5*(SEX=="Female"))
CL <- 0.02*exp(rnorm(nsub*ndose,0,.4^2))*CLCOV
KA <- 0.5*exp(rnorm(nsub*ndose,0,.4^2))
V2 <- 30*exp(rnorm(nsub*ndose,0,.4^2))*VCOV
Kin <- 0.1*exp(rnorm(nsub*ndose,0,0.4^2))
DOSE <- sort(rep(c(0,100,200,400,800,1600),nsub))
theta.all <-
cbind(KA=KA, CL=CL, V2=V2,
Q=10*CLCOV, V3=100*VCOV, V4 = 10000*VCOV, Q2 = 10*CLCOV,
Kin=Kin, Kout=0.01, EC50 = 1, Emax = 2+2*(SEX=="Female"))
# Nominal times for sampling schedule
SAMPLING <- c(c(-24,-0.1,0.1,0.5,1,2,4,8,12,18,23.9),23.9+seq(1,4)*24,
5*24 + c(0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9,95.9))
DOSING <- seq(0,5*24,24)
NT <- data.frame(NT = c(SAMPLING, DOSING), label = c(rep("SAMPLING",length(SAMPLING)),rep("DOSING",length(DOSING))))
NT <- NT[order(NT$NT),]
ev.all <- list()
for(idose in 1:length(DOSE)){
ev.all[[idose]] <- eventTable(amount.units='mg', time.units='hours')
# ev.all[[idose]]$add.dosing(dose = DOSE[idose], nbr.doses = 6, dosing.interval = 24)
# ev.all[[idose]]$add.sampling(c(c(-24,-0.1,0.1,0.5,1,2,4,8,12,18,23.9),23.9+seq(1,4)*24,
# 5*24 + c(0.1,0.5,1,2,4,8,12,18,23.9,36,47.9,71.9,95.9)))
# Include some noise in the sampling & dosing schedules
temp <- NT
temp$time <- cumsum(c(temp$NT[1] + 0.1*rnorm(1), abs(temp$NT[2:length(temp$NT)]-temp$NT[1:length(temp$NT)-1] + 0.1*rnorm(length(temp$NT)-1))))
temp$time <- temp$time - temp$time[temp$NT==0] # center at first dose
if(DOSE[idose] > 0){
ev.all[[idose]]$add.dosing(dose = DOSE[idose], start.time = temp$time[temp$label=="DOSING"])
}else{
ev.all[[idose]]$add.dosing(dose = 1e-12, start.time = temp$time[temp$label=="DOSING"])
}
ev.all[[idose]]$add.sampling(temp$time[temp$label=="SAMPLING"])
}
x.all.df <- NULL
ID = 0
for(i in 1:length(ev.all)){
ID = ID + 1
theta <- theta.all[i,]
inits <- c(depot = 0, centr = 0, peri = 0, peri2 = 0, eff=theta["Kin"]/theta["Kout"])
x <- mod1$solve(theta, ev.all[[i]], inits = inits)
x.df <- data.frame(x)
x.df$NT <- SAMPLING
temp <- data.frame(ev.all[[i]]$get.dosing())
temp$NT <- DOSING
temp[,setdiff(names(x.df),names(temp))]<- NA
x.df[,setdiff(names(temp),names(x.df))]<- NA
x.df <- rbind(x.df,temp)
x.df$ID <- ID
x.df$DOSE <- DOSE[i]
x.df$WT0 <- WT0[i]
x.df$SEX <- SEX[i]
if(is.null(x.all.df)){
x.all.df <- x.df
}else{
x.all.df <- rbind(x.all.df, x.df)
}
}
x.all.df$DV <- x.all.df$Concentration*(exp(rnorm(length(x.all.df$Concentration),0,0.6^2))) +
0.01*rnorm(length(x.all.df$Concentration))
x.all.df$DV[x.all.df$DV<0.05]=0.05
x.all.df$DV[x.all.df$Concentration==0]=NA
x.all.df$BLQ <- 0
x.all.df$BLQ[x.all.df$DV==0.05] <- 1
temp <- exp(rnorm(length(x.all.df$eff),0,0.4^2))
x.all.df$eff2 <- x.all.df$eff*temp + rnorm(length(x.all.df$eff),0,2) + 10*x.all.df$time/(72 + x.all.df$time)
temp <- runif(length(x.all.df$eff),0,1)
x.all.df$Response <- 0
x.all.df$Response[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-30))/(1+exp((x.all.df$eff2-30))) > temp] <- 1
x.all.df$Severity <- 3
x.all.df$Severity[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-18))/(1+exp((x.all.df$eff2-18))) > temp] <- 2
x.all.df$Severity[0.2*x.all.df$time/(72 + x.all.df$time) + exp((x.all.df$eff2-28))/(1+exp((x.all.df$eff2-28))) > temp] <- 1
x.all.df$Severity_label <- plyr::mapvalues(x.all.df$Severity,c(1,2,3),c("mild","moderate","severe"))
x.all.df$Severity_label <- factor(x.all.df$Severity_label, levels = unique(x.all.df$Severity_label[order(x.all.df$Severity)]))
x.all.df$Count <- NA
x.all.df$Count[!is.na(x.all.df$eff2)]<-rpois(length(na.omit(x.all.df$eff2)), na.omit(10*( 0.5/(1 + x.all.df$time/24) + 0.5*exp(-((x.all.df$eff2)-28))/(1+exp(-((x.all.df$eff2)-28))))) )
my.data <- x.all.df[!is.na(x.all.df$amt),]
my.data$CMT_label <- "Dosing"
my.data$CMT <- 1
temp <- x.all.df[is.na(x.all.df$amt),]
temp$CMT <- 2
temp$CMT_label <- "PK Concentration"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV <- temp$eff2
temp$CMT <- 3
temp$CMT_label <- "PD - Continuous"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,23.9+seq(1,9)*24),]
temp$DV <- temp$Count
temp$CMT <- 4
temp$CMT_label <- "PD - Count"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-24,23.9,23.9+seq(1,9)*24),]
temp$DV <- temp$Severity
temp$CMT <- 5
temp$CMT_label <- "PD - Ordinal"
my.data <- rbind(my.data,temp)
temp <- x.all.df[x.all.df$NT%in%c(-0.1,23.9,(23.9+seq(1,9)*24)),]
temp$DV <- temp$Response
temp$CMT <- 6
temp$CMT_label <- "PD - Binary"
my.data <- rbind(my.data,temp)
my.data$DV <- signif(my.data$DV,3)
my.data$WT0 <- signif(my.data$WT0,3)
my.data$time <- round(my.data$time,3)
my.data <- my.data[order(my.data$ID,my.data$time, my.data$CMT),]
my.data$DOSE_label <- paste(my.data$DOSE,"mg")
my.data$DOSE_label[my.data$DOSE==0] <- "Placebo"
my.data$DOSE_label <- factor(my.data$DOSE_label,levels = c("Placebo",paste(unique(my.data$DOSE[my.data$DOSE!=0]),"mg")))
my.data$DAY <- floor(my.data$NT/24)+1
my.data$DAY_label <- paste("Day",ceiling(my.data$DAY))
my.data$DAY_label[my.data$DAY<=0]<-"Baseline"
my.data$DAY_label <- factor(my.data$DAY_label,
levels = c("Baseline",paste("Day",sort(unique(ceiling(my.data$DAY))))))
my.data$CYCLE <- my.data$DAY
my.data$CYCLE[my.data$CYCLE>6] <- 6
# temp <- my.data[my.data$DAY_label=="Baseline"&my.data$CMT!=2,]
# temp$Severity0 <- temp$Severity
# temp$Response0 <- temp$Response
# temp$Count0 <- temp$Count
# temp$eff20 <- temp$eff2
#
# cnames <- c("ID","Severity0","Response0","Count0","eff20")
#
# my.data2 <- merge(my.data, unique(temp[,cnames]),by=c("ID"),all.x=TRUE)
Multiple_Ascending_Dose_Dataset <- my.data[,c("ID","time","NT","amt","DV","CMT","CMT_label","BLQ","evid","WT0","SEX","DOSE_label","DOSE","DAY","DAY_label","Response","Severity","Severity_label","Count","CYCLE")]
write.csv(Multiple_Ascending_Dose_Dataset,"../Data/Multiple_Ascending_Dose_Dataset.csv",row.names = FALSE)
# DT::datatable(Multiple_Ascending_Dose_Dataset[,c("time","NT","ID","CMT","DV","DOSE","DOSE_label","WT0","SEX","DAY","DAY_label","CMT_label")], rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE))
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset
Multiple_Ascending_Dose_Dataset2$PROFTIME <- Multiple_Ascending_Dose_Dataset2$NT - (Multiple_Ascending_Dose_Dataset2$CYCLE-1)*24
Multiple_Ascending_Dose_Dataset2$NOMTIME <- Multiple_Ascending_Dose_Dataset2$NT
Multiple_Ascending_Dose_Dataset2$YTYPE <- Multiple_Ascending_Dose_Dataset2$CMT
Multiple_Ascending_Dose_Dataset2$amt[is.na(Multiple_Ascending_Dose_Dataset2$amt)] <- 0
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset2[!(Multiple_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Multiple_Ascending_Dose_Dataset2$CMT==2),]
Multiple_Ascending_Dose_Dataset2$BLQ[Multiple_Ascending_Dose_Dataset2$DOSE_label=="Placebo"&
Multiple_Ascending_Dose_Dataset2$CMT==2] <- 1L
Multiple_Ascending_Dose_Dataset2$evid <- plyr::mapvalues(Multiple_Ascending_Dose_Dataset2$evid, c(NA,101), c(0,1))
Multiple_Ascending_Dose_Dataset2$TIMEUNIT <- "Hours"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==1] <- "mg"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==2] <- "ng/mL"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==3] <- "IU/L"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==4] <- "count"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==5] <- "severity"
Multiple_Ascending_Dose_Dataset2$EVENTU[Multiple_Ascending_Dose_Dataset2$CMT==6] <- "response"
Multiple_Ascending_Dose_Dataset2 <- Multiple_Ascending_Dose_Dataset2[,c("ID","time","NOMTIME","TIMEUNIT","amt","DV","CMT",
"CMT_label","EVENTU","BLQ","evid","WT0","SEX","DOSE_label",
"DOSE","DAY","PROFTIME","CYCLE")]
names(Multiple_Ascending_Dose_Dataset2) <- c("ID","TIME","NOMTIME","TIMEUNIT","AMT","LIDV","CMT",
"NAME","EVENTU","CENS","EVID","WEIGHTB","SEX","TRTACT",
"DOSE","PROFDAY","PROFTIME","CYCLE")
write.csv(Multiple_Ascending_Dose_Dataset2,"../Data/Multiple_Ascending_Dose_Dataset2.csv",row.names = FALSE)DT::datatable(Multiple_Ascending_Dose_Dataset2, rownames = FALSE, options = list(autoWidth = TRUE, scrollX=TRUE) )The code on this page is not evaluated during a normal site build,
for two reasons. It needs rxode2, which requires C and
Fortran compilers and is not needed by any other page. And it writes to
Data/*.csv — the committed datasets that every other page
on this site reads — so running it as part of a build would let the site
quietly regenerate its own inputs.
The datasets are therefore treated as source: generated deliberately, checked, and committed. To regenerate them:
Rscript -e 'pak::pak("rxode2")'
cd Rmarkdown
XGX_REGENERATE_DATA=true Rscript -e 'rmarkdown::render("PKPD_Datasets.Rmd")'Review the diff to Data/ before committing: the code is
seeded, but the results still depend on the R version and on the ODE
solver, so the numbers can move. This page previously used
RxODE, which was archived from CRAN on 2022-10-10; the
migration to rxode2 has not been run end to end, so expect
to verify the output the first time.
sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] survminer_0.5.2 ggpubr_1.0.0 survival_3.8-6 knitr_1.51
## [5] broom_1.0.13 caTools_1.18.4 GGally_2.4.0 DT_0.34.0
## [9] lubridate_1.9.5 forcats_1.0.1 stringr_1.6.0 purrr_1.2.2
## [13] readr_2.2.0 tibble_3.3.1 tidyverse_2.0.0 xgxr_1.1.6
## [17] zoo_1.9-0 gridExtra_2.3.1 tidyr_1.3.2 dplyr_1.2.1
## [21] ggplot2_4.0.3
##
## loaded via a namespace (and not attached):
## [1] bitops_1.1-0 gld_2.6.8 readxl_1.5.0 rlang_1.3.0
## [5] magrittr_2.0.5 otel_0.2.0 e1071_1.7-17 compiler_4.6.1
## [9] png_0.1-9 vctrs_0.7.3 pkgconfig_2.0.3 fastmap_1.2.0
## [13] backports_1.5.1 labeling_0.4.3 pander_0.6.6 rmarkdown_2.31
## [17] markdown_2.0 tzdb_0.5.0 haven_2.5.5 visdat_0.6.0
## [21] UpSetR_1.4.1 xfun_0.60 cachem_1.1.0 litedown_0.10
## [25] jsonlite_2.0.0 Deriv_4.3.0 cluster_2.1.8.2 DescTools_0.99.60
## [29] R6_2.6.1 bslib_0.12.0 stringi_1.8.9 RColorBrewer_1.1-3
## [33] car_3.1-5 boot_1.3-32 rpart_4.1.27 jquerylib_0.1.4
## [37] cellranger_1.1.0 Rcpp_1.1.2 assertthat_0.2.1 base64enc_0.1-6
## [41] splines_4.6.1 Matrix_1.7-5 nnet_7.3-20 timechange_0.4.0
## [45] tidyselect_1.2.1 abind_1.4-8 rstudioapi_0.19.0 yaml_2.3.12
## [49] minpack.lm_1.2-4 lattice_0.22-9 plyr_1.8.9 withr_3.0.3
## [53] S7_0.2.2 evaluate_1.0.5 foreign_0.8-91 ggstats_0.13.0
## [57] proxy_0.4-29 pillar_1.11.1 carData_3.0-6 corrplot_0.95
## [61] checkmate_2.3.4 generics_0.1.4 RCurl_1.98-1.19 hms_1.1.4
## [65] commonmark_2.0.0 scales_1.4.0 rootSolve_1.8.2.4 class_7.3-23
## [69] glue_1.8.1 Hmisc_5.2-6 lmom_3.3 tools_4.6.1
## [73] data.table_1.18.4 ggsignif_0.6.4 Exact_3.3 fs_2.1.0
## [77] mvtnorm_1.4-2 grid_4.6.1 crosstalk_1.2.2 colorspace_2.1-3
## [81] htmlTable_2.5.0 Formula_1.2-6 naniar_1.1.0 cli_3.6.6
## [85] expm_1.0-0 binom_1.1-2 gtable_0.3.6 rstatix_1.1.0
## [89] sass_0.4.10 digest_0.6.39 htmlwidgets_1.6.4 farver_2.1.2
## [93] htmltools_0.5.9 lifecycle_1.0.5 httr_1.4.8 MASS_7.3-65