marp

if(!require(devtools)){
    install.packages("devtools")
    library(devtools)
}

devtools::install_github("kanji709/marp")

# load R package - marp
library(marp)

# generate a small dataset
data <- rgamma(100,3,0.01)

# set parameters
m <- 10 # number of iterations for MLE optimization
t <- seq(100,200,by=10) # time intervals
B <- 99 # number of bootstraps
BB <- 99 # number of double-bootstrapps
alpha <- 0.05 # confidence level
y <- 304 # cut-off time point for probablity estimation
model_gen <- 2 # specifying the data generating model (if known)

# step one: fitting differnt renewal models
res1 <- marp::poisson_rp(dat,t,y)
res2 <- marp::gamma_rp(dat,t,m,y)
res3 <- marp::loglogis_rp(dat,t,m,y)
res4 <- marp::weibull_rp(dat,t,m,y)
res5 <- marp::lognorm_rp(dat,t,y)
res6 <- marp::bpt_rp(dat,t,m,y)

# step two: model selection and obtain model-averaged estimates
res <- marp::marp(dat,t,m,y,which.model = 2)

# step three: construct different confidence intervals (including model-averaged CIs)
ci <- marp::marp_confint(dat,m,t,B,BB,alpha,y,model_gen)