## The R worker processes in a cluster are fresh R processes that do
## not know about any non-default packages you have loaded or
## variables you have defined on the master. If you want to sue such
## packages or variables on your workers you need to make sure they
## are available.
##
## For variables, including functions, you can use clusterExport. A
## simple example:

cl <- makeCluster(2)

f1 <- function() 1
f2 <- function() 2
cl <- makeCluster(2)
clusterExport(cl, c("f1", "f2"))
parLapply(cl, 1:length(cl), function(i) i + f1() + f2())

## For packages you can use clusterEvalQ to evaluate library() calls
## on each of the workers. For example, to load the mgcp package on
## all workers use
clusterEvalQ(cl, library(mgcv))

## You can also transfer data or functions as extra arguments to your
## work function, as done here:
m <- function(x) sin(6 * x)
gamsim <- function(n, m, s) {
    x <- seq(0, 1, length.out = n)
    y <- rnorm(m(x), s)
    gam(y ~ s(x))
}
parLapply(cl, rep(50, length(cl)), gamsim, m, 0.1)

## Don't forget to shut down the cluster.
stopCluster(cl)