Prodest package in R for TFP estimation, gives different results

I have the following issue: each time I run the estimation of the TFP by using prodest package in R 4.0.3, I obtain different coefficients before the variables as well as omega variable is different:

The dataset is here: https://drive.google.com/file/d/11cZ4ozUOtKxlXR3fLtjaQjsr9G-z54yN/view?usp=sharing

The code is the following:

remove(list=ls())

library(plm)
library(dplyr)
library(ggplot2)
library(prodest)


pckg<-c("plm","readxl","dplyr","ggplot2","prodest")
install.packages(c("plm","readxl","dplyr","ggplot2","prodest"))
lapply(pckg, require, character.only = TRUE)

# Set the working directory
setwd("------")

# Downloading the survey data
Data <- read.csv("test.csv", header=TRUE, sep=",")
str(Data)

Data$ID<-as.numeric(as.factor(Data$ID))

summary(Data)

# Creating a panel data frame

DataA <- Data %>% 
  filter(NACE == 'A') %>% 
  filter(VA > 0, L > 0, FA > 0, M > 0, Turn > 0, TFA > 0) %>%
  mutate(ID = ID,
         Year = Year,
         l = log(L),
         va = log(VA),
         k = log(TFA),
         m = log(M),
         turn = log(Turn),
         ta = log(TA))


  
####################################################################################################################################
mod2LP <- prodest::prodestLP(DataA$va, fX = DataA$l, sX = DataA$k, pX = DataA$m, idvar = DataA$ID, timevar = DataA$Year, 
                             R = 100, cX = NULL, opt = "optim", theta0 = NULL, cluster = NULL, tol = 1e-100, exit = FALSE)  
mod2LP
omegaLP <- prodest::omega(mod2LP)
summary(mod2LP)
summary(omegaLP)

DataA$omega <- prodest::omega(mod2LP) 

hist(omegaLP)


mod2OP <- prodest::prodestOP(DataA11$turn, fX = DataA11$l, sX = DataA11$k, pX = DataA11$m, idvar = DataA11$ID, timevar = DataA11$Year, 
                             R = 100, cX = NULL, opt = "optim", theta0 = NULL, cluster = NULL, tol = 1e-100, exit = FALSE)  

mod2OP
omegaOP <- prodest::omega(mod2OP)
summary(mod2OP)
summary(omegaOP)



mod2ACF <-  prodest::prodestACF(DataA$va, fX = DataA$l, sX = DataA$k, pX = DataA$m, idvar = DataA$ID, timevar = DataA$Year, 
                                R = 100, cX = NULL, opt = 'optim', theta0 = NULL, cluster = NULL)  

mod2ACF
omegaACF <- prodest::omega(mod2ACF)
summary(mod2ACF)
summary(omegaACF)

DataA$omega <- prodest::omega(mod2ACF) 



mod2W <-  prodest::prodestWRDG(DataA$va, fX = DataA$l, sX = DataA$k, pX = DataA$m, idvar = DataA$ID, timevar = DataA$Year, 
                                   cX = NULL)  
mod2W
omegaW <- prodest::omega(mod2W)
summary(mod2W)
summary(omegaW)

####################################################################################################################################

Now if you run ACF or Wooldridge methods several times, you will obtain different coefficients as well as TFP measures.

Any ideas on how to solve it?