scaclust <- function (x, centers, iter.max = 100, verbose = FALSE,                    method = "ad", theta= NULL) {  xrows <- dim(x)[1]  xcols <- dim(x)[2]  xold <- x  perm <- sample(xrows)  x <- x[perm, ]  ## initial values are given  if (is.matrix(centers))     ncenters <- dim(centers)[1]  else {   ## take centers random vectors as initial values    ncenters <- centers    centers <- x[rank(runif(xrows))[1:ncenters], ]+0.001  }##  method <- pmatch(method, c("ad", "mtv", "sand","ml"))  method <- pmatch(method, c("ad", "mtv", "sand","mlm"))  if (is.na(method))     stop("invalid clustering method")  if (method == -1)     stop("ambiguous clustering method")    if (method == 1) {    beta <- 1/xcols    taf <- 0 }  if (method == 2) {    beta <- 0.5    taf <- xcols/2 }  if (method == 3) {    beta <- 1/xcols    taf <- 1 }  if (method == 4){    beta <- 0.0    taf <- -1 }    ##initialize theta ## if (method != 4){  if (missing(theta))    theta <- rep(1.0,ncenters)  else    theta <- as.double(theta)    ##}      initcenters <- centers  ## dist <- matrix(0, xrows, ncenters)  ## necessary for empty clusters  pos <- as.factor(1:ncenters)  rownames(centers) <- pos  iter <- integer(1)  ##  if ((method == 1) || (method == 2) || (method == 3)){    retval <- .C("common", xrows = as.integer(xrows),                 xcols = as.integer(xcols),                  x = as.double(x), ncenters = as.integer(ncenters),                  centers = as.double(centers),                  itermax = as.integer(iter.max), iter = as.integer(iter),                  verbose = as.integer(verbose), U=double(xrows*ncenters),                 beta=as.double(beta), taf=as.double(taf),                 theta=as.double(theta),ermin=double(1))  centers <- matrix(retval$centers, ncol = xcols, dimnames = dimnames(initcenters))    U <- retval$U  U <- matrix(U, ncol=ncenters)  ##  clusterU <- max.col(U)  clusterU <- apply(U,1,which.max)  clusterU <- clusterU[order(perm)]  U <- U[order(perm),]    clustersize <- as.integer(table(clusterU))    retval <- list(centers = centers, size = clustersize,                 cluster = clusterU, iter = retval$iter - 1, membership=U,                 withinerror = retval$ermin, call = match.call())    class(retval) <- c("fclust")  return(retval)}