% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/computeCVError.R
\name{computeCVError}
\alias{computeCVError}
\title{Compute CV Error (Internal)}
\usage{
computeCVError(cv_data, active_set, cv_fit, cv_loss)
}
\arguments{
\item{cv_data}{List of fold data (x_train, y_train, x_val, y_val).}

\item{active_set}{Integer vector of active predictors.}

\item{cv_fit}{Character. Fitting method: "ls" or "huber" (IRLS).}

\item{cv_loss}{Character. Loss function: "huber", "trimmed", or "mse".}
}
\value{
Numeric. The averaged cross-validation error.
}
\description{
Evaluates the cross-validation error of a given active set using a hyper-fast 
Cholesky solver (with optional Huber IRLS robust fitting) and robust validation loss.
}
\keyword{internal}
