Nothing
"jsmurf" <-
function(y, x = 1:length(y), x0 = 2 * x[1] - x[2], q, alpha = 0.05, r = 4e3, lengths = 2^(floor(log2(length(y))):floor(log2(max(length(param$kern) + 1, 1 / param$param$cutoff)))), param, rm.out = FALSE, jumpint = confband, confband = FALSE)
{
# compute standard deviation
sdi <- sdrobnorm(y, lag = length(param$kern) + 1)
# determine and remove outliers
if(rm.out) {
cutmin <- quantile(y, 1e-4) + qnorm(1 / length(y)) * sdi
cutmax <- quantile(y, 1 - 1e-4) - qnorm(1 / length(y)) * sdi
outlier <- y < cutmin | y > cutmax
if(any(outlier)) {
ycut <- y[-neighbours(which(outlier), 1:length(y), length(param$kern) - 1)]
xcut <- x[-neighbours(which(outlier), 1:length(y), length(param$kern) - 1)]
} else {
ycut <- y
xcut <- x
}
} else {
ycut <- y
xcut <- x
}
# compute quantile
if(missing(q)) {
if(is.null(r)) stop("q or r need to be specified!")
q <- kMRC.quant(1 - alpha, length(y), r, param$kern, lengths)
} else {
alpha <- NA
}
# compute bounds
bs <- bounds.MRC(ycut, q = q, lengths = lengths, family = 'gaussKern', param = param)
# compute bounded solution
allblocks <- stepbound.default(ycut, bounds = bs, x = xcut, x0 = x0, family = 'gaussKern', param = param, refit = FALSE, jumpint = jumpint, confband = confband)
if(!is.na(alpha)) attr(allblocks, "alpha") <- alpha
attr(allblocks, "q") <- q
attr(allblocks, "sd") <- sdi
allblocks
}
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