`tsallisaccum` <-
    function (x, scales = seq(0, 2, 0.2), permutations = 100, raw = FALSE,
              subset, ...)
{
    if (!missing(subset))
        x <- subset(x, subset)
    x <- as.matrix(x)
    n <- nrow(x)
    p <- ncol(x)
    if (p == 1) {
        x <- t(x)
        n <- nrow(x)
        p <- ncol(x)
    }
    pmat <- getPermuteMatrix(permutations, n)
    m <- length(scales)
    result <- array(dim = c(n, m, permutations))
    dimnames(result) <- list(pooled.sites = c(1:n), scale = scales, 
        permutation = c(1:permutations))
    for (k in 1:permutations) {
        result[, , k] <- as.matrix(tsallis((apply(x[pmat[k,], 
            ], 2, cumsum)), scales = scales, ...))
    }
    if (raw) {
        if (m == 1) {
            result <- result[, 1, ]
        }
    }
    else {
        tmp <- array(dim = c(n, m, 6))
        for (i in 1:n) {
            for (j in 1:m) {
                tmp[i, j, 1] <- mean(result[i, j, 1:permutations])
                tmp[i, j, 2] <- sd(result[i, j, 1:permutations])
                tmp[i, j, 3] <- min(result[i, j, 1:permutations])
                tmp[i, j, 4] <- max(result[i, j, 1:permutations])
                tmp[i, j, 5] <- quantile(result[i, j, 1:permutations], 
                  0.025)
                tmp[i, j, 6] <- quantile(result[i, j, 1:permutations], 
                  0.975)
            }
        }
        result <- tmp
        dimnames(result) <- list(pooled.sites = c(1:n), scale = scales, 
            c("mean", "stdev", "min", "max", "Qnt 0.025", "Qnt 0.975"))
    }
    attr(result, "control") <- attr(pmat, "control")
    class(result) <- c("tsallisaccum", "renyiaccum", class(result))
    result
}
