Options for caching / memoization / hashing in R

I did not have luck with memoise because it gave a ‘too deep recursive’ problem to some functions of a package I tried it with. With R.cache I had better luck. Following is more annotated code I adapted from the R.cache documentation. The code shows different options for doing caching:

# Workaround to avoid question when loading R.cache library
dir.create(path="~/.Rcache", showWarnings=F) 
library("R.cache")
setCacheRootPath(path="./.Rcache") # Create .Rcache at current working dir
# In case we need the cache path, but not used in this example.
cache.root = getCacheRootPath() 
simulate <- function(mean, sd) {
    # 1. Try to load cached data, if already generated
    key <- list(mean, sd)
    data <- loadCache(key)
    if (!is.null(data)) {
        cat("Loaded cached data\n")
        return(data);
    }
    # 2. If not available, generate it.
    cat("Generating data from scratch...")
    data <- rnorm(1000, mean=mean, sd=sd)
    Sys.sleep(1) # Emulate slow algorithm
    cat("ok\n")
    saveCache(data, key=key, comment="simulate()")
    data;
}
data <- simulate(2.3, 3.0)
data <- simulate(2.3, 3.5)
a = 2.3
b = 3.0
data <- simulate(a, b) # Will load cached data, params are checked by value
# Clean up
file.remove(findCache(key=list(2.3,3.0)))
file.remove(findCache(key=list(2.3,3.5)))

simulate2 <- function(mean, sd) {
    data <- rnorm(1000, mean=mean, sd=sd)
    Sys.sleep(1) # Emulate slow algorithm
    cat("Done generating data from scratch\n")
    data;
}
# Easy step to memoize a function
# aslo possible to resassign function name.
This would work with any functions from external packages. 
mzs <- addMemoization(simulate2)

data <- mzs(2.3, 3.0)
data <- mzs(2.3, 3.5)
data <- mzs(2.3, 3.0) # Will load cached data
# aslo possible to resassign function name.
# but different memoizations of the same 
# function will return the same cache result
# if input params are the same
simulate2 <- addMemoization(simulate2)
data <- simulate2(2.3, 3.0)

# If the expression being evaluated depends on
# "input" objects, then these must be be specified
# explicitly as "key" objects.
for (ii in 1:2) {
    for (kk in 1:3) {
        cat(sprintf("Iteration #%d:\n", kk))
        res <- evalWithMemoization({
            cat("Evaluating expression...")
            a <- kk
            Sys.sleep(1)
            cat("done\n")
            a
        }, key=list(kk=kk))
        # expressions inside 'res' are skipped on the repeated run
        print(res)
        # Sanity checks
        stopifnot(a == kk)
        # Clean up
        rm(a)
    } # for (kk ...)
} # for (ii ...)