Fix a type and change `MAT` to `x` to refer to input matrix.

bigcorPar.r

bigcorPar <- function(x, nblocks = 10, verbose = TRUE, ncore="all", ...){
  library(ff, quietly = TRUE)
  require(doMC)
	if(ncore=="all"){
		ncore = multicore:::detectCores()
		registerDoMC(cores = ncore)
	} else{
		registerDoMC(cores = ncore)
	}

	NCOL <- ncol(x)

	## test if ncol(x) %% nblocks gives remainder 0
	if (NCOL %% nblocks != 0){stop("Choose different 'nblocks' so that ncol(x) %% nblocks = 0!")}

	## preallocate square matrix of dimension
	## ncol(x) in 'ff' single format
	corMAT <- ff(vmode = "single", dim = c(NCOL, NCOL))

	## split column numbers into 'nblocks' groups
	SPLIT <- split(1:NCOL, rep(1:nblocks, each = NCOL/nblocks))

	## create all unique combinations of blocks
	COMBS <- expand.grid(1:length(SPLIT), 1:length(SPLIT))
	COMBS <- t(apply(COMBS, 1, sort))
	COMBS <- unique(COMBS)

	## iterate through each block combination, calculate correlation matrix
	## between blocks and store them in the preallocated matrix on both
	## symmetric sides of the diagonal
	results <- foreach(i = 1:nrow(COMBS)) %dopar% {
		COMB <- COMBS[i, ]
		G1 <- SPLIT[[COMB[1]]]
		G2 <- SPLIT[[COMB[2]]]
		if (verbose) cat("Block", COMB[1], "with Block", COMB[2], "\n")
		flush.console()
		COR <- cor(x[, G1], x[, G2], ...)
		corMAT[G1, G2] <- COR
		corMAT[G2, G1] <- t(COR)
		COR <- NULL
	}

	gc()
	return(corMAT)
}