isomle<-function (x) 
{
    if (is.complex(x)) {
        tem <- array(0, c(nrow(x), 2, ncol(x)))
        tem[, 1, ] <- Re(x)
        tem[, 2, ] <- Im(x)
        x <- tem
    }
    k <- dim(x)[1]
    m <- dim(x)[2]
    n <- dim(x)[3]
    if (m > 2) {
        print("Only valid for 2D data")
    }
    if (m == 2) {
        pm <- rep(0, times = 2 * k - 3)
        tem <- procrustes2d(x)
        tem1 <- bookstein.shpv(tem$mshape)
        sigm <- sum(diag(var(tem$tan)))/(n - 1)/2
        pm[1:(k - 2)] <- tem1[3:k, 1]
        pm[(k - 1):(2 * k - 4)] <- tem1[3:k, 2]
        pm[2 * k - 3] <- 10
        ans <- nlm(objfuniso, hessian = TRUE, pm, uu = x)
        out <- list(code = 0, mshape = 0, tau = 0, kappa = 0, 
            varcov = 0, gradient = 0)
        mn <- matrix(0, k, 2)
        mn[1, 1] <- -0.5
        mn[2, 1] <- 0.5
        mn[3:k, 1] <- ans$estimate[1:(k - 2)]
        mn[3:k, 2] <- ans$estimate[(k - 1):(2 * k - 4)]
        out$mshape <- mn
        out$loglike <- -ans$minimum
        out$code <- ans$code
        out$gradient <- ans$gradient
        out$tau <- sqrt(1/ans$estimate[2 * k - 3]^2)
        out$kappa <- centroid.size(mn)^2/(4 * out$tau^2)
        out$varcov <- solve(ans$hessian)
        out$se <- c(sqrt(diag(out$varcov)))
        out$se[2*k-3] <- out$se[2*k-3] *out$tau**2
        out
    }
}