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diff --git a/man/AgghooCV.Rd b/man/AgghooCV.Rd
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+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/R6_AgghooCV.R
+\name{AgghooCV}
+\alias{AgghooCV}
+\title{R6 class with agghoo functions fit() and predict().}
+\description{
+Class encapsulating the methods to run to obtain the best predictor
+from the list of models (see 'Model' class).
+}
+\section{Methods}{
+\subsection{Public methods}{
+\itemize{
+\item \href{#method-new}{\code{AgghooCV$new()}}
+\item \href{#method-fit}{\code{AgghooCV$fit()}}
+\item \href{#method-predict}{\code{AgghooCV$predict()}}
+\item \href{#method-clone}{\code{AgghooCV$clone()}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-new"></a>}}
+\if{latex}{\out{\hypertarget{method-new}{}}}
+\subsection{Method \code{new()}}{
+Create a new AgghooCV object.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$new(data, target, task, gmodel, quality = NA)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{data}}{Matrix or data.frame}
+
+\item{\code{target}}{Vector of targets (generally numeric or factor)}
+
+\item{\code{task}}{"regression" or "classification"}
+
+\item{\code{gmodel}}{Generic model returning a predictive function}
+
+\item{\code{quality}}{Function assessing the quality of a prediction;
+quality(y1, y2) --> real number}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-fit"></a>}}
+\if{latex}{\out{\hypertarget{method-fit}{}}}
+\subsection{Method \code{fit()}}{
+Fit an agghoo model.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$fit(
+  CV = list(type = "MC", V = 10, test_size = 0.2, shuffle = TRUE),
+  mode = "agghoo"
+)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{CV}}{List describing cross-validation to run. Slots:
+- type: 'vfold' or 'MC' for Monte-Carlo (default: MC)
+- V: number of runs (default: 10)
+- test_size: percentage of data in the test dataset, for MC
+  (irrelevant for V-fold). Default: 0.2.
+- shuffle: wether or not to shuffle data before V-fold.
+  Irrelevant for Monte-Carlo; default: TRUE}
+
+\item{\code{mode}}{"agghoo" or "standard" (for usual cross-validation)}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-predict"></a>}}
+\if{latex}{\out{\hypertarget{method-predict}{}}}
+\subsection{Method \code{predict()}}{
+Predict an agghoo model (after calling fit())
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$predict(X, weight = "uniform")}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{X}}{Matrix or data.frame to predict}
+
+\item{\code{weight}}{"uniform" (default) or "quality" to weight votes or
+average models performances (TODO: bad idea?!)}
+}
+\if{html}{\out{</div>}}
+}
+}
+\if{html}{\out{<hr>}}
+\if{html}{\out{<a id="method-clone"></a>}}
+\if{latex}{\out{\hypertarget{method-clone}{}}}
+\subsection{Method \code{clone()}}{
+The objects of this class are cloneable with this method.
+\subsection{Usage}{
+\if{html}{\out{<div class="r">}}\preformatted{AgghooCV$clone(deep = FALSE)}\if{html}{\out{</div>}}
+}
+
+\subsection{Arguments}{
+\if{html}{\out{<div class="arguments">}}
+\describe{
+\item{\code{deep}}{Whether to make a deep clone.}
+}
+\if{html}{\out{</div>}}
+}
+}
+}