#' is.compliant(test_dataset)
#'
#' @param x A TRONCO compliant dataset.

")){
if(type[1] %in% c("mse", "mspe")){
err.fun <- function(tsb) {

= function(e) NULL)
err.funs = which(!(funs %in% names(fun.list)))
if (length(err.funs) > 0L)

# --
if(has.g2) {
aggr.res.x <- aplot.statErr(x[idx], fun=aggr.fun, err.fun=aggr.err.fun,

=TRUE,p.cutoff=0.5,err.fun=NULL,rand=NA,...){
check.snplist<-TRUE
if(is.null(list.snp) & is.null(list.ia

`getCall` <-
function(call,n.obs){
fun<-as.character(call$err.fun)

), function(x) order(x, decreasing = FALSE))
znn <- data.frame(z)
z <- lapply(data.frame(D), function

. It now forces interpolation with NN.
if (nnas > 0)
nn <- knnidw(1, rmax = .Machine$double.xmax)

("stepwise.eval.stud.expr")
if (!is.null(seed))
set.seed(seed)

silent = FALSE) {
is.compliant(x, err.fun='events.selection: input')
dataset = x$genotypes

("stepwise.eval.stud.expr")
if (!is.null(seed))
set.seed(seed)

<- zu[1:2]
znn <- zu[(l-1):l]
} else {

type <- attr(x, "entry")
key <- attr(x, "key")
y <- as.list(x)

= .Machine$double.xmax)
sub_grid <- data.frame(X = grid$X[isna],  Y = grid$Y[isna])
znn <- nn(ground, sub_grid)