How to do double (nested) parSapply in R?

Hello,

I have a data orig.raw that contains 8 predictors and 1 outcome variable.
I'm trying to run simulation (bootstrap) and create, let's say, 10
confidence intervals for coefficients estimated by LASSO. I did it with
regular function replicate, but now I want to do it by using parallel
programming. Here is my code:

cl <- makeCluster(detectCores())
clusterEvalQ(cl,library(glmnet))
clusterEvalQ(cl,library(MASS))
clusterExport(cl,c("orig.raw"))

pp=parSapply(cl,1:10,function(i,data=orig.raw,...){
  library(parallel)
  cl <- makeCluster(detectCores())
  clusterEvalQ(cl,library(glmnet))
  clusterEvalQ(cl,library(MASS))
  clusterExport(cl,c("orig.raw"))

  repl=parSapply(cl=cl,1:10,function(i,data=orig.raw,...){
    s1=data[sample(nrow(data),size=500,replace=TRUE),]
    cross=cv.glmnet(s1[,1:8],s1[,9])
    penalty <- cross$lambda.min
    fit=glmnet(s1[,1:8],s1[,9],alpha=1,lambda=penalty)
    coe=coef(fit)
    m=as(coe, "matrix")
    return(m)
  })

stopCluster(cl)

  mr=t(matrix(repl,nrow = 9,ncol=10))
  means=colMeans(mr)
  std=apply(mr, 2, sd)
  lb=means-1.96*std;
  ub=means+1.96*std;
  ind=t(as.numeric({beta>lb & beta<ub}))
  return(ind)})
stopCluster(cl)

And here is the error I'm getting

Error in checkForRemoteErrors(val) : 8 nodes produced errors; first
error: comparison (6) is possible only for atomic and list types

If I run only function repl - it works and I get the matrix that contains
coefficients from 10 runs.

Can you please help me to solve the problem?
Regards,
Ariel

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