From: Benjamin Auder <benjamin.auder@somewhere>
Date: Tue, 25 Apr 2023 07:48:36 +0000 (+0200)
Subject: Preparing for CRAN upload
X-Git-Url: https://git.auder.net/variants/Chakart/css/assets/current/doc/%7B%7B?a=commitdiff_plain;h=HEAD;p=agghoo.git

Preparing for CRAN upload
---

diff --git a/.gitignore b/.gitignore
index 812f17e..ae9cf7e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,2 @@
-/data/
-/doc/
+/man/
 .RData
diff --git a/R/compareTo.R b/R/compareTo.R
index fe5b24d..0eb517c 100644
--- a/R/compareTo.R
+++ b/R/compareTo.R
@@ -203,7 +203,6 @@ compareTo <- function(
 compareMulti <- function(
   data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
 ) {
-  base::require(parallel)
   if (is.na(nc))
     nc <- parallel::detectCores()
 
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/DESCRIPTION b/agghoo.Rcheck/00_pkg_src/agghoo/DESCRIPTION
deleted file mode 100644
index 21f9ca3..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/DESCRIPTION
+++ /dev/null
@@ -1,26 +0,0 @@
-Package: agghoo
-Title: Aggregated Hold-Out Cross Validation
-Date: 2022-08-30
-Version: 0.1-0
-Description: The 'agghoo' procedure is an alternative to usual cross-validation.
-    Instead of choosing the best model trained on V subsamples, it determines
-    a winner model for each subsample, and then aggregate the V outputs.
-    For the details, see "Aggregated hold-out" by Guillaume Maillard,
-    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
-    published in Journal of Machine Learning Research 22(20):1--55.
-Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
-    Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
-    Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
-    Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
-    Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
-Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
-Depends: R (>= 3.5.0)
-Imports: class, parallel, R6, rpart, FNN
-Suggests: roxygen2
-URL: https://git.auder.net/?p=agghoo.git
-License: MIT + file LICENSE
-RoxygenNote: 7.2.1
-Collate: 'compareTo.R' 'agghoo.R' 'R6_AgghooCV.R' 'R6_Model.R'
-        'checks.R' 'utils.R' 'A_NAMESPACE.R'
-NeedsCompilation: no
-Packaged: 2022-09-09 15:45:56 UTC; auder
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/LICENSE b/agghoo.Rcheck/00_pkg_src/agghoo/LICENSE
deleted file mode 100644
index 094ff81..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/LICENSE
+++ /dev/null
@@ -1,2 +0,0 @@
-YEAR: 2021-2022
-COPYRIGHT HOLDER: Sylvain Arlot, Benjamin Auder, Melina Gallopin, Matthieu Lerasle, Guillaume Maillard
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/NAMESPACE b/agghoo.Rcheck/00_pkg_src/agghoo/NAMESPACE
deleted file mode 100644
index 7bbddef..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/NAMESPACE
+++ /dev/null
@@ -1,13 +0,0 @@
-# Generated by roxygen2: do not edit by hand
-
-export(AgghooCV)
-export(Model)
-export(agghoo)
-export(compareMulti)
-export(compareRange)
-export(compareTo)
-importFrom(FNN,knn.reg)
-importFrom(R6,R6Class)
-importFrom(class,knn)
-importFrom(rpart,rpart)
-importFrom(stats,ppr)
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/A_NAMESPACE.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/A_NAMESPACE.R
deleted file mode 100644
index 0466833..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/A_NAMESPACE.R
+++ /dev/null
@@ -1,7 +0,0 @@
-#' @include utils.R
-#' @include checks.R
-#' @include R6_Model.R
-#' @include R6_AgghooCV.R
-#' @include agghoo.R
-#' @include compareTo.R
-NULL
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R
deleted file mode 100644
index 328c141..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_AgghooCV.R
+++ /dev/null
@@ -1,115 +0,0 @@
-#' @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).
-#'
-#' @importFrom R6 R6Class
-#'
-#' @export
-AgghooCV <- R6::R6Class("AgghooCV",
-  public = list(
-    #' @description Create a new AgghooCV object.
-    #' @param data Matrix or data.frame
-    #' @param target Vector of targets (generally numeric or factor)
-    #' @param task "regression" or "classification".
-    #'             Default: classification if target not numeric.
-    #' @param gmodel Generic model returning a predictive function
-    #'               Default: tree if mixed data, knn/ppr otherwise.
-    #' @param loss Function assessing the error of a prediction
-    #'             Default: error rate or mean(abs(error)).
-    initialize = function(data, target, task, gmodel, loss) {
-      private$data <- data
-      private$target <- target
-      private$task <- task
-      private$gmodel <- gmodel
-      private$loss <- loss
-    },
-    #' @description Fit an agghoo model.
-    #' @param CV List describing cross-validation to run. Slots: \cr
-    #'          - type: 'vfold' or 'MC' for Monte-Carlo (default: MC) \cr
-    #'          - V: number of runs (default: 10) \cr
-    #'          - test_size: percentage of data in the test dataset, for MC
-    #'            (irrelevant for V-fold). Default: 0.2. \cr
-    #'          - shuffle: wether or not to shuffle data before V-fold.
-    #'            Irrelevant for Monte-Carlo; default: TRUE \cr
-    #'        Default (if NULL): type="MC", V=10, test_size=0.2
-    fit = function(CV = NULL) {
-      CV <- checkCV(CV)
-      n <- nrow(private$data)
-      shuffle_inds <- NULL
-      if (CV$type == "vfold" && CV$shuffle)
-        shuffle_inds <- sample(n, n)
-      # Result: list of V predictive models (+ parameters for info)
-      private$pmodels <- list()
-      for (v in seq_len(CV$V)) {
-        # Prepare train / test data and target, from full dataset.
-        # dataHO: "data Hold-Out" etc.
-        test_indices <- get_testIndices(n, CV, v, shuffle_inds)
-        d <- splitTrainTest(private$data, private$target, test_indices)
-        best_model <- NULL
-        best_error <- Inf
-        for (p in seq_len(private$gmodel$nmodels)) {
-          model_pred <- private$gmodel$get(d$dataTrain, d$targetTrain, p)
-          prediction <- model_pred(d$dataTest)
-          error <- private$loss(prediction, d$targetTest)
-          if (error <= best_error) {
-            newModel <- list(model=model_pred, param=private$gmodel$getParam(p))
-            if (error == best_error)
-              best_model[[length(best_model)+1]] <- newModel
-            else {
-              best_model <- list(newModel)
-              best_error <- error
-            }
-          }
-        }
-        # Choose a model at random in case of ex-aequos
-        private$pmodels[[v]] <- best_model[[ sample(length(best_model),1) ]]
-      }
-    },
-    #' @description Predict an agghoo model (after calling fit())
-    #' @param X Matrix or data.frame to predict
-    predict = function(X) {
-      if (!is.matrix(X) && !is.data.frame(X))
-        stop("X: matrix or data.frame")
-      if (!is.list(private$pmodels)) {
-        print("Please call $fit() method first")
-        return (invisible(NULL))
-      }
-      V <- length(private$pmodels)
-      oneLineX <- X[1,]
-      if (is.matrix(X))
-        # HACK: R behaves differently with data frames and matrices.
-        oneLineX <- t(as.matrix(oneLineX))
-      if (length(private$pmodels[[1]]$model(oneLineX)) >= 2)
-        # Soft classification:
-        return (Reduce("+", lapply(private$pmodels, function(m) m$model(X))) / V)
-      n <- nrow(X)
-      all_predictions <- as.data.frame(matrix(nrow=n, ncol=V))
-      for (v in 1:V)
-        all_predictions[,v] <- private$pmodels[[v]]$model(X)
-      if (private$task == "regression")
-        # Easy case: just average each row
-        return (rowMeans(all_predictions))
-      # "Hard" classification:
-      apply(all_predictions, 1, function(row) {
-        t <- table(row)
-        # Next lines in case of ties (broken at random)
-        tmax <- max(t)
-        sample( names(t)[which(t == tmax)], 1 )
-      })
-    },
-    #' @description Return the list of V best parameters (after calling fit())
-    getParams = function() {
-      lapply(private$pmodels, function(m) m$param)
-    }
-  ),
-  private = list(
-    data = NULL,
-    target = NULL,
-    task = NULL,
-    gmodel = NULL,
-    loss = NULL,
-    pmodels = NULL
-  )
-)
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_Model.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_Model.R
deleted file mode 100644
index d48825e..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/R6_Model.R
+++ /dev/null
@@ -1,157 +0,0 @@
-#' @title R6 class representing a (generic) model.
-#'
-#' @description
-#' "Model" class, containing a (generic) learning function, which from
-#' data + target [+ params] returns a prediction function X --> y.
-#' Parameters for cross-validation are either provided or estimated.
-#' Model family can be chosen among "tree", "ppr" and "knn" for now.
-#'
-#' @importFrom FNN knn.reg
-#' @importFrom class knn
-#' @importFrom stats ppr
-#' @importFrom rpart rpart
-#'
-#' @export
-Model <- R6::R6Class("Model",
-  public = list(
-    #' @field nmodels Number of parameters (= number of [predictive] models)
-    nmodels = NA,
-    #' @description Create a new generic model.
-    #' @param data Matrix or data.frame
-    #' @param target Vector of targets (generally numeric or factor)
-    #' @param task "regression" or "classification"
-    #' @param gmodel Generic model returning a predictive function; chosen
-    #'               automatically given data and target nature if not provided.
-    #' @param params List of parameters for cross-validation (each defining a model)
-    initialize = function(data, target, task, gmodel = NULL, params = NULL) {
-      if (is.null(gmodel)) {
-        # (Generic) model not provided
-        all_numeric <- is.numeric(as.matrix(data))
-        if (!all_numeric)
-          # At least one non-numeric column: use trees
-          gmodel = "tree"
-        else
-          # Numerical data
-          gmodel = ifelse(task == "regression", "ppr", "knn")
-      }
-      if (is.null(params))
-        # Here, gmodel is a string (= its family),
-        # because a custom model must be given with its parameters.
-        params <- as.list(private$getParams(gmodel, data, target, task))
-      private$params <- params
-      if (is.character(gmodel))
-        gmodel <- private$getGmodel(gmodel, task)
-      private$gmodel <- gmodel
-      self$nmodels <- length(private$params)
-    },
-    #' @description
-    #' Returns the model at index "index", trained on dataHO/targetHO.
-    #' @param dataHO Matrix or data.frame
-    #' @param targetHO Vector of targets (generally numeric or factor)
-    #' @param index Index of the model in 1...nmodels
-    get = function(dataHO, targetHO, index) {
-      private$gmodel(dataHO, targetHO, private$params[[index]])
-    },
-    #' @description
-    #' Returns the parameter at index "index".
-    #' @param index Index of the model in 1...nmodels
-    getParam = function(index) {
-      private$params[[index]]
-    }
-  ),
-  private = list(
-    # No need to expose model or parameters list
-    gmodel = NULL,
-    params = NULL,
-    # Main function: given a family, return a generic model, which in turn
-    # will output a predictive model from data + target + params.
-    getGmodel = function(family, task) {
-      if (family == "tree") {
-        function(dataHO, targetHO, param) {
-          base::require(rpart)
-          method <- ifelse(task == "classification", "class", "anova")
-          if (is.null(colnames(dataHO)))
-            colnames(dataHO) <- paste0("V", 1:ncol(dataHO))
-          df <- data.frame(cbind(dataHO, target=targetHO))
-          model <- rpart::rpart(target ~ ., df, method=method, control=list(cp=param))
-          if (task == "regression")
-            type <- "vector"
-          else {
-            if (is.null(dim(targetHO)))
-              type <- "class"
-            else
-              type <- "prob"
-          }
-          function(X) {
-            if (is.null(colnames(X)))
-              colnames(X) <- paste0("V", 1:ncol(X))
-            predict(model, as.data.frame(X), type=type)
-          }
-        }
-      }
-      else if (family == "ppr") {
-        function(dataHO, targetHO, param) {
-          model <- stats::ppr(dataHO, targetHO, nterms=param)
-          function(X) predict(model, X)
-        }
-      }
-      else if (family == "knn") {
-        if (task == "classification") {
-          function(dataHO, targetHO, param) {
-            base::require(class)
-            function(X) class::knn(dataHO, X, cl=targetHO, k=param)
-          }
-        }
-        else {
-          function(dataHO, targetHO, param) {
-            base::require(FNN)
-            function(X) FNN::knn.reg(dataHO, X, y=targetHO, k=param)$pred
-          }
-        }
-      }
-    },
-    # Return a default list of parameters, given a gmodel family
-    getParams = function(family, data, target, task) {
-      if (family == "tree") {
-        # Run rpart once to obtain a CV grid for parameter cp
-        base::require(rpart)
-        df <- data.frame(cbind(data, target=target))
-        ctrl <- list(
-          cp = 0,
-          minsplit = 2,
-          minbucket = 1,
-          xval = 0)
-        method <- ifelse(task == "classification", "class", "anova")
-        r <- rpart(target ~ ., df, method=method, control=ctrl)
-        cps <- r$cptable[-1,1]
-        if (length(cps) <= 1)
-          stop("No cross-validation possible: select another model")
-        if (length(cps) <= 11)
-          return (cps)
-        step <- (length(cps) - 1) / 10
-        cps[unique(round(seq(1, length(cps), step)))]
-      }
-      else if (family == "ppr")
-        # This is nterms in ppr() function
-        1:10
-      else if (family == "knn") {
-        n <- nrow(data)
-        # Choose ~10 NN values
-        K <- length(unique(target))
-        if (n <= 10)
-          return (1:(n-1))
-        sqrt_n <- sqrt(n)
-        step <- (2*sqrt_n - 1) / 10
-        grid <- unique(round(seq(1, 2*sqrt_n, step)))
-        if (K == 2) {
-          # Common binary classification case: odd number of neighbors
-          for (i in 2:11) {
-            if (grid[i] %% 2 == 0)
-              grid[i] <- grid[i] + 1 #arbitrary choice
-          }
-        }
-        grid
-      }
-    }
-  )
-)
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/agghoo.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/agghoo.R
deleted file mode 100644
index 48ac741..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/agghoo.R
+++ /dev/null
@@ -1,58 +0,0 @@
-#' agghoo
-#'
-#' Run the (core) agghoo procedure.
-#' Arguments specify the list of models, their parameters and the
-#' cross-validation settings, among others.
-#'
-#' @param data Data frame or matrix containing the data in lines.
-#' @param target The target values to predict. Generally a vector,
-#'        but possibly a matrix in the case of "soft classification".
-#' @param task "classification" or "regression". Default:
-#'        regression if target is numerical, classification otherwise.
-#' @param gmodel A "generic model", which is a function returning a predict
-#'        function (taking X as only argument) from the tuple
-#'        (dataHO, targetHO, param), where 'HO' stands for 'Hold-Out',
-#'        referring to cross-validation. Cross-validation is run on an array
-#'        of 'param's. See params argument. Default: see R6::Model.
-#' @param params A list of parameters. Often, one list cell is just a
-#'        numerical value, but in general it could be of any type.
-#'        Default: see R6::Model.
-#' @param loss A function assessing the error of a prediction.
-#'        Arguments are y1 and y2 (comparing a prediction to known values).
-#'        loss(y1, y2) --> real number (error). Default: see R6::AgghooCV.
-#'
-#' @return
-#' An R6::AgghooCV object o. Then, call o$fit() and finally o$predict(newData)
-#'
-#' @examples
-#' # Regression:
-#' a_reg <- agghoo(iris[,-c(2,5)], iris[,2])
-#' a_reg$fit()
-#' pr <- a_reg$predict(iris[,-c(2,5)] + rnorm(450, sd=0.1))
-#' # Classification
-#' a_cla <- agghoo(iris[,-5], iris[,5])
-#' a_cla$fit()
-#' pc <- a_cla$predict(iris[,-5] + rnorm(600, sd=0.1))
-#'
-#' @seealso Function \code{\link{compareTo}}
-#'
-#' @references
-#' Guillaume Maillard, Sylvain Arlot, Matthieu Lerasle. "Aggregated hold-out".
-#' Journal of Machine Learning Research 22(20):1--55, 2021.
-#'
-#' @export
-agghoo <- function(
-  data, target, task = NULL, gmodel = NULL, params = NULL, loss = NULL
-) {
-	# Args check:
-  checkDaTa(data, target)
-  task <- checkTask(task, target)
-  modPar <- checkModPar(gmodel, params)
-  loss <- checkLoss(loss, task)
-
-  # Build Model object (= list of parameterized models)
-  model <- Model$new(data, target, task, modPar$gmodel, modPar$params)
-
-  # Return AgghooCV object, to run and predict
-  AgghooCV$new(data, target, task, model, loss)
-}
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/checks.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/checks.R
deleted file mode 100644
index a19d55f..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/checks.R
+++ /dev/null
@@ -1,102 +0,0 @@
-# Internal usage: check and fill arguments with default values.
-
-defaultLoss_classif <- function(y1, y2) {
-  if (is.null(dim(y1)))
-    # Standard case: "hard" classification
-    mean(y1 != y2)
-  else {
-    # "Soft" classification: predict() outputs a probability matrix
-    # In this case "target" could be in matrix form.
-    if (!is.null(dim(y2)))
-      mean(rowSums(abs(y1 - y2)))
-    else {
-      # Or not: y2 is a "factor".
-      y2 <- as.character(y2)
-      # NOTE: the user should provide target in matrix form because
-      # matching y2 with columns is rather inefficient!
-      names <- colnames(y1)
-      positions <- list()
-      for (idx in seq_along(names))
-        positions[[ names[idx] ]] <- idx
-      mean(vapply(
-        seq_along(y2),
-        function(idx) sum(abs(y1[idx,] - positions[[ y2[idx] ]])),
-        0))
-    }
-  }
-}
-
-defaultLoss_regress <- function(y1, y2) {
-  mean(abs(y1 - y2))
-}
-
-# TODO: allow strings like "MSE", "abs" etc
-checkLoss <- function(loss, task) {
-  if (!is.null(loss) && !is.function(loss))
-    stop("loss: function(y1, y2) --> Real")
-  if (is.null(loss)) {
-    loss <- if (task == "classification") {
-      defaultLoss_classif
-    } else {
-      defaultLoss_regress
-    }
-  }
-  loss
-}
-
-checkCV <- function(CV) {
-  if (is.null(CV))
-    CV <- list(type="MC", V=10, test_size=0.2, shuffle=TRUE)
-  else {
-    if (!is.list(CV))
-      stop("CV: list of type('MC'|'vfold'), V(integer, [test_size, shuffle]")
-    if (is.null(CV$type)) {
-      warning("CV$type not provided: set to MC")
-      CV$type <- "MC"
-    }
-    if (is.null(CV$V)) {
-      warning("CV$V not provided: set to 10")
-      CV$V <- 10
-    }
-    if (CV$type == "MC" && is.null(CV$test_size))
-      CV$test_size <- 0.2
-    if (CV$type == "vfold" && is.null(CV$shuffle))
-      CV$shuffle <- TRUE
-  }
-  CV
-}
-
-checkDaTa <- function(data, target) {
-  if (!is.data.frame(data) && !is.matrix(data))
-    stop("data: data.frame or matrix")
-  if (is.data.frame(target) || is.matrix(target)) {
-    if (!is.numeric(target))
-      stop("multi-columns target must be a probability matrix")
-    if (nrow(target) != nrow(data) || ncol(target) == 1)
-      stop("target probability matrix does not match data size")
-  }
-  else if (!is.numeric(target) && !is.factor(target) && !is.character(target))
-    stop("target: numeric, factor or character vector")
-}
-
-checkTask <- function(task, target) {
-  if (!is.null(task))
-    task <- match.arg(task, c("classification", "regression"))
-  ifelse(is.numeric(target), "regression", "classification")
-}
-
-checkModPar <- function(gmodel, params) {
-  if (is.character(gmodel))
-    gmodel <- match.arg(gmodel, c("knn", "ppr", "rf", "tree"))
-  else if (!is.null(gmodel) && !is.function(gmodel))
-    stop("gmodel: function(dataHO, targetHO, param) --> function(X) --> y")
-  if (is.numeric(params) || is.character(params))
-    params <- as.list(params)
-  if (!is.list(params) && !is.null(params))
-    stop("params: numerical, character, or list (passed to model)")
-  if (is.function(gmodel) && !is.list(params))
-    stop("params must be provided when using a custom model")
-  if (is.list(params) && is.null(gmodel))
-    stop("model (or family) must be provided when using custom params")
-  list(gmodel=gmodel, params=params)
-}
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R
deleted file mode 100644
index fe5b24d..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/compareTo.R
+++ /dev/null
@@ -1,247 +0,0 @@
-#' standardCV_core
-#'
-#' Cross-validation method, added here as an example.
-#' Parameters are described in ?agghoo and ?AgghooCV
-standardCV_core <- function(data, target, task, gmodel, params, loss, CV) {
-  n <- nrow(data)
-  shuffle_inds <- NULL
-  if (CV$type == "vfold" && CV$shuffle)
-    shuffle_inds <- sample(n, n)
-  list_testinds <- list()
-  for (v in seq_len(CV$V))
-    list_testinds[[v]] <- get_testIndices(n, CV, v, shuffle_inds)
-  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
-  best_error <- Inf
-  best_p <- NULL
-  for (p in seq_len(gmodel$nmodels)) {
-    error <- Reduce('+', lapply(seq_len(CV$V), function(v) {
-      testIdx <- list_testinds[[v]]
-      d <- splitTrainTest(data, target, testIdx)
-      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
-      prediction <- model_pred(d$dataTest)
-      loss(prediction, d$targetTest)
-    }) )
-    if (error <= best_error) {
-      if (error == best_error)
-        best_p[[length(best_p)+1]] <- p
-      else {
-        best_p <- list(p)
-        best_error <- error
-      }
-    }
-  }
-  chosenP <- best_p[[ sample(length(best_p), 1) ]]
-  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
-}
-
-#' CVvoting_core
-#'
-#' "voting" cross-validation method, added here as an example.
-#' Parameters are described in ?agghoo and ?AgghooCV
-CVvoting_core <- function(data, target, task, gmodel, params, loss, CV) {
-  CV <- checkCV(CV)
-  n <- nrow(data)
-  shuffle_inds <- NULL
-  if (CV$type == "vfold" && CV$shuffle)
-    shuffle_inds <- sample(n, n)
-  gmodel <- agghoo::Model$new(data, target, task, gmodel, params)
-  bestP <- rep(0, gmodel$nmodels)
-  for (v in seq_len(CV$V)) {
-    test_indices <- get_testIndices(n, CV, v, shuffle_inds)
-    d <- splitTrainTest(data, target, test_indices)
-    best_p <- NULL
-    best_error <- Inf
-    for (p in seq_len(gmodel$nmodels)) {
-      model_pred <- gmodel$get(d$dataTrain, d$targetTrain, p)
-      prediction <- model_pred(d$dataTest)
-      error <- loss(prediction, d$targetTest)
-      if (error <= best_error) {
-        if (error == best_error)
-          best_p[[length(best_p)+1]] <- p
-        else {
-          best_p <- list(p)
-          best_error <- error
-        }
-      }
-    }
-    for (p in best_p)
-      bestP[p] <- bestP[p] + 1
-  }
-  # Choose a param at random in case of ex-aequos:
-  maxP <- max(bestP)
-  chosenP <- sample(which(bestP == maxP), 1)
-  list(model=gmodel$get(data, target, chosenP), param=gmodel$getParam(chosenP))
-}
-
-#' standardCV_run
-#'
-#' Run and eval the standard cross-validation procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-standardCV_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
-  args <- list(...)
-  task <- checkTask(args$task, targetTrain)
-  modPar <- checkModPar(args$gmodel, args$params)
-  loss <- checkLoss(args$loss, task)
-  CV <- checkCV(args$CV)
-  s <- standardCV_core(
-    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
-  if (verbose)
-    print(paste( "Parameter:", s$param ))
-  p <- s$model(dataTest)
-  err <- floss(p, targetTest)
-  if (verbose)
-    print(paste("error CV:", err))
-  invisible(err)
-}
-
-#' CVvoting_run
-#'
-#' Run and eval the voting cross-validation procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-CVvoting_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
-  args <- list(...)
-  task <- checkTask(args$task, targetTrain)
-  modPar <- checkModPar(args$gmodel, args$params)
-  loss <- checkLoss(args$loss, task)
-  CV <- checkCV(args$CV)
-  s <- CVvoting_core(
-    dataTrain, targetTrain, task, modPar$gmodel, modPar$params, loss, CV)
-  if (verbose)
-    print(paste( "Parameter:", s$param ))
-  p <- s$model(dataTest)
-  err <- floss(p, targetTest)
-  if (verbose)
-    print(paste("error CV:", err))
-  invisible(err)
-}
-
-#' agghoo_run
-#'
-#' Run and eval the agghoo procedure.
-#' Parameters are rather explicit except "floss", which corresponds to the
-#' "final" loss function, applied to compute the error on testing dataset.
-agghoo_run <- function(
-  dataTrain, dataTest, targetTrain, targetTest, floss, verbose, ...
-) {
-  args <- list(...)
-  CV <- checkCV(args$CV)
-  # Must remove CV arg, or agghoo will complain "error: unused arg"
-  args$CV <- NULL
-  a <- do.call(agghoo, c(list(data=dataTrain, target=targetTrain), args))
-  a$fit(CV)
-  if (verbose) {
-    print("Parameters:")
-    print(unlist(a$getParams()))
-  }
-  pa <- a$predict(dataTest)
-  err <- floss(pa, targetTest)
-  if (verbose)
-    print(paste("error agghoo:", err))
-  invisible(err)
-}
-
-#' compareTo
-#'
-#' Compare a list of learning methods (or run only one), on data/target.
-#'
-#' @param data Data matrix or data.frame
-#' @param target Target vector (generally)
-#' @param method_s Either a single function, or a list
-#'                 (examples: agghoo_run, standardCV_run)
-#' @param rseed Seed of the random generator (-1 means "random seed")
-#' @param floss Loss function to compute the error on testing dataset.
-#' @param verbose TRUE to request methods to be verbose.
-#' @param ... arguments passed to method_s function(s)
-#'
-#' @export
-compareTo <- function(
-  data, target, method_s, rseed=-1, floss=NULL, verbose=TRUE, ...
-) {
-  if (rseed >= 0)
-    set.seed(rseed)
-  n <- nrow(data)
-  test_indices <- sample( n, round(n / ifelse(n >= 500, 10, 5)) )
-  d <- splitTrainTest(data, target, test_indices)
-
-  # Set error function to be used on model outputs (not in core method)
-  task <- checkTask(list(...)$task, target)
-  if (is.null(floss)) {
-    floss <- function(y1, y2) {
-      ifelse(task == "classification", mean(y1 != y2), mean(abs(y1 - y2)))
-    }
-  }
-
-  # Run (and compare) all methods:
-  runOne <- function(o) {
-    o(d$dataTrain, d$dataTest, d$targetTrain, d$targetTest, floss, verbose, ...)
-  }
-  errors <- c()
-  if (is.list(method_s))
-    errors <- sapply(method_s, runOne)
-  else if (is.function(method_s))
-    errors <- runOne(method_s)
-  invisible(errors)
-}
-
-#' compareMulti
-#'
-#' Run compareTo N times in parallel.
-#'
-#' @inheritParams compareTo
-#' @param N Number of calls to method(s)
-#' @param nc Number of cores. Set to parallel::detectCores() if undefined.
-#'           Set it to any value <=1 to say "no parallelism".
-#' @param verbose TRUE to print task numbers and "Errors:" in the end.
-#'
-#' @export
-compareMulti <- function(
-  data, target, method_s, N=100, nc=NA, floss=NULL, verbose=TRUE, ...
-) {
-  base::require(parallel)
-  if (is.na(nc))
-    nc <- parallel::detectCores()
-
-  # "One" comparison for each method in method_s (list)
-  compareOne <- function(n) {
-    if (verbose)
-      print(n)
-    compareTo(data, target, method_s, n, floss, verbose=FALSE, ...)
-  }
-
-  errors <- if (nc >= 2) {
-    parallel::mclapply(1:N, compareOne, mc.cores = nc)
-  } else {
-    lapply(1:N, compareOne)
-  }
-  if (verbose)
-    print("Errors:")
-  Reduce('+', errors) / N
-}
-
-#' compareRange
-#'
-#' Run compareMulti on several values of the parameter V.
-#'
-#' @inheritParams compareMulti
-#' @param V_range Values of V to be tested.
-#'
-#' @export
-compareRange <- function(
-  data, target, method_s, N=100, nc=NA, floss=NULL, V_range=c(10,15,20), ...
-) {
-  args <- list(...)
-  # Avoid warnings if V is left unspecified:
-  CV <- suppressWarnings( checkCV(args$CV) )
-  errors <- lapply(V_range, function(V) {
-    args$CV$V <- V
-    do.call(compareMulti, c(list(data=data, target=target, method_s=method_s,
-                                 N=N, nc=nc, floss=floss, verbose=F), args))
-  })
-  print(paste(V_range, errors))
-}
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R b/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R
deleted file mode 100644
index 823b123..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/R/utils.R
+++ /dev/null
@@ -1,30 +0,0 @@
-# Helper for cross-validation: return the next test indices.
-get_testIndices <- function(n, CV, v, shuffle_inds) {
-  if (CV$type == "vfold") {
-    # Slice indices (optionnally shuffled)
-    first_index = round((v-1) * n / CV$V) + 1
-    last_index = round(v * n / CV$V)
-    test_indices = first_index:last_index
-    if (!is.null(shuffle_inds))
-      test_indices <- shuffle_inds[test_indices]
-  }
-  else
-    # Monte-Carlo cross-validation
-    test_indices = sample(n, round(n * CV$test_size))
-  test_indices
-}
-
-# Helper which split data into training and testing parts.
-splitTrainTest <- function(data, target, testIdx) {
-  dataTrain <- data[-testIdx,]
-  targetTrain <- target[-testIdx]
-  dataTest <- data[testIdx,]
-  targetTest <- target[testIdx]
-  # [HACK] R will cast 1-dim matrices into vectors:
-  if (!is.matrix(dataTrain) && !is.data.frame(dataTrain))
-    dataTrain <- as.matrix(dataTrain)
-  if (!is.matrix(dataTest) && !is.data.frame(dataTest))
-    dataTest <- as.matrix(dataTest)
-  list(dataTrain=dataTrain, targetTrain=targetTrain,
-       dataTest=dataTest, targetTest=targetTest)
-}
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/README.md b/agghoo.Rcheck/00_pkg_src/agghoo/README.md
deleted file mode 100644
index 337abcb..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/README.md
+++ /dev/null
@@ -1,15 +0,0 @@
-# agghoo
-
-R package for model selection based on aggregation.
-Alternative to standard cross-validation.
-
-## Install the package
-
-From GitHub: `devtools::install_github("yagu0/agghoo")`
-
-Locally, in a terminal: `R CMD INSTALL .`
-
-## Use the package
-
-    library(agghoo)
-    ?agghoo
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/TODO b/agghoo.Rcheck/00_pkg_src/agghoo/TODO
deleted file mode 100644
index f197d8a..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/TODO
+++ /dev/null
@@ -1,2 +0,0 @@
-Support des valeurs manquantes (cf. mlbench::Ozone dataset)
-Méthode pour données mixtes ? (que tree actuellement)
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R b/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R
deleted file mode 100644
index 7fae2ce..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/example/example.R
+++ /dev/null
@@ -1,43 +0,0 @@
-library(agghoo)
-
-data(iris) #already there
-library(mlbench)
-data(PimaIndiansDiabetes)
-
-# Run only agghoo on iris dataset (split into train/test, etc).
-# Default parameters: see ?agghoo and ?AgghooCV
-compareTo(iris[,-5], iris[,5], agghoo_run)
-
-# Run both agghoo and standard CV, specifiying some parameters.
-compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run), gmodel="tree")
-compareTo(iris[,-5], iris[,5], list(agghoo_run, standardCV_run),
-          gmodel="knn", params=c(3, 7, 13, 17, 23, 31),
-          CV = list(type="vfold", V=5, shuffle=T))
-
-# Run both agghoo and standard CV, averaging errors over N=10 runs
-# (possible for a single method but wouldn't make much sense...).
-compareMulti(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
-             list(agghoo_run, standardCV_run), N=10, gmodel="rf")
-
-# Compare several values of V
-compareRange(PimaIndiansDiabetes[,-9], PimaIndiansDiabetes[,9],
-             list(agghoo_run, standardCV_run), N=10, V_range=c(10, 20, 30))
-
-# For example to use average of squared differences.
-# Default is "mean(abs(y1 - y2))".
-loss2 <- function(y1, y2) mean((y1 - y2)^2)
-
-# In regression on artificial datasets (TODO: real data?)
-data <- mlbench.twonorm(300, 3)$x
-target <- rowSums(data)
-compareMulti(data, target, list(agghoo_run, standardCV_run),
-             N=10, gmodel="tree", params=c(1, 3, 5, 7, 9), loss=loss2,
-             CV = list(type="MC", V=12, test_size=0.3))
-
-compareMulti(data, target, list(agghoo_run, standardCV_run),
-             N=10, floss=loss2, CV = list(type="vfold", V=10, shuffle=F))
-
-# Random tests to check that method doesn't fail in 1D case
-M <- matrix(rnorm(200), ncol=2)
-compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="knn")
-compareTo(as.matrix(M[,-2]), M[,2], list(agghoo_run, standardCV_run), gmodel="tree")
diff --git a/agghoo.Rcheck/00_pkg_src/agghoo/test/TODO b/agghoo.Rcheck/00_pkg_src/agghoo/test/TODO
deleted file mode 100644
index 50acca1..0000000
--- a/agghoo.Rcheck/00_pkg_src/agghoo/test/TODO
+++ /dev/null
@@ -1 +0,0 @@
-Some unit tests?
diff --git a/agghoo.Rcheck/00check.log b/agghoo.Rcheck/00check.log
deleted file mode 100644
index 684daae..0000000
--- a/agghoo.Rcheck/00check.log
+++ /dev/null
@@ -1,52 +0,0 @@
-* using log directory ‘/home/auder/repos/agghoo/agghoo.Rcheck’
-* using R version 4.2.1 (2022-06-23)
-* using platform: x86_64-pc-linux-gnu (64-bit)
-* using session charset: UTF-8
-* checking for file ‘agghoo/DESCRIPTION’ ... OK
-* this is package ‘agghoo’ version ‘0.1-0’
-* checking package namespace information ... OK
-* checking package dependencies ... OK
-* checking if this is a source package ... OK
-* checking if there is a namespace ... OK
-* checking for executable files ... OK
-* checking for hidden files and directories ... OK
-* checking for portable file names ... OK
-* checking for sufficient/correct file permissions ... OK
-* checking whether package ‘agghoo’ can be installed ... OK
-* checking installed package size ... OK
-* checking package directory ... OK
-* checking DESCRIPTION meta-information ... OK
-* checking top-level files ... OK
-* checking for left-over files ... OK
-* checking index information ... OK
-* checking package subdirectories ... OK
-* checking R files for non-ASCII characters ... OK
-* checking R files for syntax errors ... OK
-* checking whether the package can be loaded ... OK
-* checking whether the package can be loaded with stated dependencies ... OK
-* checking whether the package can be unloaded cleanly ... OK
-* checking whether the namespace can be loaded with stated dependencies ... OK
-* checking whether the namespace can be unloaded cleanly ... OK
-* checking loading without being on the library search path ... OK
-* checking dependencies in R code ... OK
-* checking S3 generic/method consistency ... OK
-* checking replacement functions ... OK
-* checking foreign function calls ... OK
-* checking R code for possible problems ... NOTE
-compareMulti: no visible binding for global variable ‘parallel’
-Undefined global functions or variables:
-  parallel
-* checking for missing documentation entries ... WARNING
-Undocumented code objects:
-  ‘AgghooCV’ ‘Model’ ‘agghoo’ ‘compareMulti’ ‘compareRange’ ‘compareTo’
-All user-level objects in a package should have documentation entries.
-See chapter ‘Writing R documentation files’ in the ‘Writing R
-Extensions’ manual.
-* checking examples ... NONE
-* checking PDF version of manual ... WARNING
-LaTeX errors when creating PDF version.
-This typically indicates Rd problems.
-* checking PDF version of manual without index ... ERROR
-Re-running with no redirection of stdout/stderr.
-* DONE
-Status: 1 ERROR, 2 WARNINGs, 1 NOTE
diff --git a/agghoo.Rcheck/00install.out b/agghoo.Rcheck/00install.out
deleted file mode 100644
index 4ec7d20..0000000
--- a/agghoo.Rcheck/00install.out
+++ /dev/null
@@ -1,12 +0,0 @@
-* installing *source* package ‘agghoo’ ...
-** using staged installation
-** R
-** byte-compile and prepare package for lazy loading
-** help
-No man pages found in package  ‘agghoo’ 
-*** installing help indices
-** building package indices
-** testing if installed package can be loaded from temporary location
-** testing if installed package can be loaded from final location
-** testing if installed package keeps a record of temporary installation path
-* DONE (agghoo)
diff --git a/agghoo.Rcheck/Rdlatex.log b/agghoo.Rcheck/Rdlatex.log
deleted file mode 100644
index ed3d8b1..0000000
--- a/agghoo.Rcheck/Rdlatex.log
+++ /dev/null
@@ -1,22 +0,0 @@
-Hmm ... looks like a package
-Converting parsed Rd's to LaTeX Creating pdf output from LaTeX ...
-warning: kpathsea: configuration file texmf.cnf not found in these directories: /usr/bin:/usr/bin/share/texmf-local/web2c:/usr/bin/share/texmf-dist/web2c:/usr/bin/share/texmf/web2c:/usr/bin/texmf-local/web2c:/usr/bin/texmf-dist/web2c:/usr/bin/texmf/web2c:/usr:/usr/share/texmf-local/web2c:/usr/share/texmf-dist/web2c:/usr/share/texmf/web2c:/usr/texmf-local/web2c:/usr/texmf-dist/web2c:/usr/texmf/web2c://texmf-local/web2c:/://share/texmf-local/web2c://share/texmf-dist/web2c://share/texmf/web2c://texmf-local/web2c://texmf-dist/web2c://texmf/web2c.
-This is pdfTeX, Version 3.141592653-2.6-1.40.24 (TeX Live 2022/Arch Linux) (preloaded format=pdflatex)
-
-kpathsea: Running mktexfmt pdflatex.fmt
-mktexfmt: No such file or directory
-I can't find the format file `pdflatex.fmt'!
-Warning in file(con, "r") :
-  cannot open file 'Rd2.log': No such file or directory
-Error in file(con, "r") : cannot open the connection
-warning: kpathsea: configuration file texmf.cnf not found in these directories: /usr/bin:/usr/bin/share/texmf-local/web2c:/usr/bin/share/texmf-dist/web2c:/usr/bin/share/texmf/web2c:/usr/bin/texmf-local/web2c:/usr/bin/texmf-dist/web2c:/usr/bin/texmf/web2c:/usr:/usr/share/texmf-local/web2c:/usr/share/texmf-dist/web2c:/usr/share/texmf/web2c:/usr/texmf-local/web2c:/usr/texmf-dist/web2c:/usr/texmf/web2c://texmf-local/web2c:/://share/texmf-local/web2c://share/texmf-dist/web2c://share/texmf/web2c://texmf-local/web2c://texmf-dist/web2c://texmf/web2c.
-This is pdfTeX, Version 3.141592653-2.6-1.40.24 (TeX Live 2022/Arch Linux) (preloaded format=pdflatex)
-
-kpathsea: Running mktexfmt pdflatex.fmt
-mktexfmt: No such file or directory
-I can't find the format file `pdflatex.fmt'!
-Warning in file(con, "r") :
-  cannot open file 'Rd2.log': No such file or directory
-Error in file(con, "r") : cannot open the connection
-Error in running tools::texi2pdf()
-You may want to clean up by 'rm -Rf /tmp/RtmpIZpCnq/Rd2pdf1084ce20004'
diff --git a/agghoo.Rcheck/agghoo-manual.tex b/agghoo.Rcheck/agghoo-manual.tex
deleted file mode 100644
index 8a561f0..0000000
--- a/agghoo.Rcheck/agghoo-manual.tex
+++ /dev/null
@@ -1,44 +0,0 @@
-\nonstopmode{}
-\documentclass[letterpaper]{book}
-\usepackage[times,hyper]{Rd}
-\usepackage{makeidx}
-\usepackage[utf8]{inputenc} % @SET ENCODING@
-% \usepackage{graphicx} % @USE GRAPHICX@
-\makeindex{}
-\begin{document}
-\chapter*{}
-\begin{center}
-{\textbf{\huge Package `agghoo'}}
-\par\bigskip{\large \today}
-\end{center}
-\ifthenelse{\boolean{Rd@use@hyper}}{\hypersetup{pdftitle = {agghoo: Aggregated Hold-Out Cross Validation}}}{}
-\begin{description}
-\raggedright{}
-\item[Title]\AsIs{Aggregated Hold-Out Cross Validation}
-\item[Date]\AsIs{2022-08-30}
-\item[Version]\AsIs{0.1-0}
-\item[Description]\AsIs{The 'agghoo' procedure is an alternative to usual cross-validation.
-Instead of choosing the best model trained on V subsamples, it determines
-a winner model for each subsample, and then aggregate the V outputs.
-For the details, see ``Aggregated hold-out'' by Guillaume Maillard,
-Sylvain Arlot, Matthieu Lerasle (2021) <}\Rhref{https://arxiv.org/abs/1909.04890}{arXiv:1909.04890}\AsIs{>
-published in Journal of Machine Learning Research 22(20):1--55.}
-\item[Author]\AsIs{Sylvain Arlot }\email{sylvain.arlot@universite-paris-saclay.fr}\AsIs{ [cph,ctb],
-Benjamin Auder }\email{benjamin.auder@universite-paris-saclay.fr}\AsIs{ [aut,cre,cph],
-Melina Gallopin }\email{melina.gallopin@universite-paris-saclay.fr}\AsIs{ [cph,ctb],
-Matthieu Lerasle }\email{matthieu.lerasle@universite-paris-saclay.fr}\AsIs{ [cph,ctb],
-Guillaume Maillard }\email{guillaume.maillard@uni.lu}\AsIs{ [cph,ctb]}
-\item[Maintainer]\AsIs{Benjamin Auder }\email{benjamin.auder@universite-paris-saclay.fr}\AsIs{}
-\item[Depends]\AsIs{R (>= 3.5.0)}
-\item[Imports]\AsIs{class, parallel, R6, rpart, FNN}
-\item[Suggests]\AsIs{roxygen2}
-\item[URL]\AsIs{}\url{https://git.auder.net/?p=agghoo.git}\AsIs{}
-\item[License]\AsIs{MIT + file LICENSE}
-\item[RoxygenNote]\AsIs{7.2.1}
-\item[Collate]\AsIs{'compareTo.R' 'agghoo.R' 'R6_AgghooCV.R' 'R6_Model.R'
-'checks.R' 'utils.R' 'A_NAMESPACE.R'}
-\item[NeedsCompilation]\AsIs{no}
-\end{description}
-\Rdcontents{\R{} topics documented:}
-\printindex{}
-\end{document}
diff --git a/agghoo.Rcheck/agghoo/DESCRIPTION b/agghoo.Rcheck/agghoo/DESCRIPTION
deleted file mode 100644
index cb86199..0000000
--- a/agghoo.Rcheck/agghoo/DESCRIPTION
+++ /dev/null
@@ -1,27 +0,0 @@
-Package: agghoo
-Title: Aggregated Hold-Out Cross Validation
-Date: 2022-08-30
-Version: 0.1-0
-Description: The 'agghoo' procedure is an alternative to usual cross-validation.
-    Instead of choosing the best model trained on V subsamples, it determines
-    a winner model for each subsample, and then aggregate the V outputs.
-    For the details, see "Aggregated hold-out" by Guillaume Maillard,
-    Sylvain Arlot, Matthieu Lerasle (2021) <arXiv:1909.04890>
-    published in Journal of Machine Learning Research 22(20):1--55.
-Author: Sylvain Arlot <sylvain.arlot@universite-paris-saclay.fr> [cph,ctb],
-    Benjamin Auder <benjamin.auder@universite-paris-saclay.fr> [aut,cre,cph],
-    Melina Gallopin <melina.gallopin@universite-paris-saclay.fr> [cph,ctb],
-    Matthieu Lerasle <matthieu.lerasle@universite-paris-saclay.fr> [cph,ctb],
-    Guillaume Maillard <guillaume.maillard@uni.lu> [cph,ctb]
-Maintainer: Benjamin Auder <benjamin.auder@universite-paris-saclay.fr>
-Depends: R (>= 3.5.0)
-Imports: class, parallel, R6, rpart, FNN
-Suggests: roxygen2
-URL: https://git.auder.net/?p=agghoo.git
-License: MIT + file LICENSE
-RoxygenNote: 7.2.1
-Collate: 'compareTo.R' 'agghoo.R' 'R6_AgghooCV.R' 'R6_Model.R'
-        'checks.R' 'utils.R' 'A_NAMESPACE.R'
-NeedsCompilation: no
-Packaged: 2022-09-09 15:45:56 UTC; auder
-Built: R 4.2.1; ; 2022-09-09 15:46:05 UTC; unix
diff --git a/agghoo.Rcheck/agghoo/LICENSE b/agghoo.Rcheck/agghoo/LICENSE
deleted file mode 100644
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--- a/agghoo.Rcheck/agghoo/LICENSE
+++ /dev/null
@@ -1,2 +0,0 @@
-YEAR: 2021-2022
-COPYRIGHT HOLDER: Sylvain Arlot, Benjamin Auder, Melina Gallopin, Matthieu Lerasle, Guillaume Maillard
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diff --git a/agghoo.Rcheck/agghoo/NAMESPACE b/agghoo.Rcheck/agghoo/NAMESPACE
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-# Generated by roxygen2: do not edit by hand
-
-export(AgghooCV)
-export(Model)
-export(agghoo)
-export(compareMulti)
-export(compareRange)
-export(compareTo)
-importFrom(FNN,knn.reg)
-importFrom(R6,R6Class)
-importFrom(class,knn)
-importFrom(rpart,rpart)
-importFrom(stats,ppr)
diff --git a/agghoo.Rcheck/agghoo/R/agghoo b/agghoo.Rcheck/agghoo/R/agghoo
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-#  File share/R/nspackloader.R
-#  Part of the R package, https://www.R-project.org
-#
-#  Copyright (C) 1995-2012 The R Core Team
-#
-#  This program is free software; you can redistribute it and/or modify
-#  it under the terms of the GNU General Public License as published by
-#  the Free Software Foundation; either version 2 of the License, or
-#  (at your option) any later version.
-#
-#  This program is distributed in the hope that it will be useful,
-#  but WITHOUT ANY WARRANTY; without even the implied warranty of
-#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-#  GNU General Public License for more details.
-#
-#  A copy of the GNU General Public License is available at
-#  https://www.r-project.org/Licenses/
-
-local({
-    info <- loadingNamespaceInfo()
-    pkg <- info$pkgname
-    ns <- .getNamespace(as.name(pkg))
-    if (is.null(ns))
-        stop("cannot find namespace environment for ", pkg, domain = NA);
-    dbbase <- file.path(info$libname, pkg, "R", pkg)
-    lazyLoad(dbbase, ns, filter = function(n) n != ".__NAMESPACE__.")
-})
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diff --git a/agghoo.Rcheck/agghoo/html/00Index.html b/agghoo.Rcheck/agghoo/html/00Index.html
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-<!DOCTYPE html>
-<html>
-<head><title>R: Aggregated Hold-Out Cross Validation</title>
-<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
-<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes" />
-<link rel="stylesheet" type="text/css" href="R.css" />
-</head><body><div class="container">
-<h1> Aggregated Hold-Out Cross Validation
-<img class="toplogo" src="../../../doc/html/Rlogo.svg" alt="[R logo]" />
-</h1>
-<hr/>
-<div style="text-align: center;">
-<a href="../../../doc/html/packages.html"><img class="arrow" src="../../../doc/html/left.jpg" alt="[Up]" /></a>
-<a href="../../../doc/html/index.html"><img class="arrow" src="../../../doc/html/up.jpg" alt="[Top]" /></a>
-</div><h2>Documentation for package &lsquo;agghoo&rsquo; version 0.1-0</h2>
-
-<ul><li><a href="../DESCRIPTION">DESCRIPTION file</a>.</li>
-</ul>
-
-<h2>Help Pages</h2>
-
-
-There are no help pages in this package
-</div></body></html>
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-@media screen {
-    .container {
-	padding-right: 10px;
-	padding-left: 10px;
-	margin-right: auto;
-	margin-left: auto;
-	max-width: 900px;
-    }
-}
-
-.rimage img { /* from knitr - for examples and demos */
-    width: 96%;
-    margin-left: 2%;
-} 	
-
-.katex { font-size: 1.1em; }
-
-code {
-    color: inherit;
-    background: inherit;
-}
-
-body {
-    line-height: 1.4;
-    background: white;
-    color: black;
-}
-
-a:link {
-    background: white;
-    color: blue;
-}
-
-a:visited {
-    background: white;
-    color: rgb(50%, 0%, 50%);
-}
-
-h1 {
-    background: white;
-    color: rgb(55%, 55%, 55%);
-    font-family: monospace;
-    font-size: 1.4em; /* x-large; */
-    text-align: center;
-}
-
-h2 {
-    background: white;
-    color: rgb(40%, 40%, 40%);
-    font-family: monospace;
-    font-size: 1.2em; /* large; */
-    text-align: center;
-}
-
-h3 {
-    background: white;
-    color: rgb(40%, 40%, 40%);
-    font-family: monospace;
-    font-size: 1.2em; /* large; */
-}
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-    background: white;
-    color: rgb(40%, 40%, 40%);
-    font-family: monospace;
-    font-style: italic;
-    font-size: 1.2em; /* large; */
-}
-
-h5 {
-    background: white;
-    color: rgb(40%, 40%, 40%);
-    font-family: monospace;
-}
-
-h6 {
-    background: white;
-    color: rgb(40%, 40%, 40%);
-    font-family: monospace;
-    font-style: italic;
-}
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-img.toplogo {
-    width: 4em;
-    vertical-align: middle;
-}
-
-img.arrow {
-    width: 30px;
-    height: 30px;
-    border: 0;
-}
-
-span.acronym {
-    font-size: small;
-}
-
-span.env {
-    font-family: monospace;
-}
-
-span.file {
-    font-family: monospace;
-}
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-span.option{
-    font-family: monospace;
-}
-
-span.pkg {
-    font-weight: bold;
-}
-
-span.samp{
-    font-family: monospace;
-}
-
-div.vignettes a:hover {
-    background: rgb(85%, 85%, 85%);
-}
diff --git a/agghoo_0.1-0.tar.gz b/agghoo_0.1-0.tar.gz
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diff --git a/test/TODO b/test/TODO
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+++ /dev/null
@@ -1 +0,0 @@
-Some unit tests?