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library(mlr3benchmark)

## create mlr3benchmark object
bma <- as.BenchmarkAggr(bm)

## run global Friedman test
bma$friedman_test()

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## load ggplot2 for autoplots
library(ggplot2)

## critical difference diagrams for IGS
autoplot(bma, meas = "graf", type = "cd", ratio = 1/3, p.value = 0.1)

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raincloudplot <- function(data, y, fill, x = fill, cols = NULL,
                          boxplots = c("below", "in", "none")) {
  
  require(dplyr)
  require(ggplot2)
  source("https://raw.githubusercontent.com/datavizpyr/data/master/half_flat_violinplot.R")
  
  boxplots <- match.arg(boxplots)

  p <- ggplot(data, aes_string(y = y, x = x, fill = fill)) +

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`%=%` <- function(l, r) {
    l <- trimws(strsplit(l, ",", TRUE)[[1]])
    if (all(l == "USE.NAMES") || all(l == "*") || all(l == "?")) {
      stopifnot(length(names(r)) > 0)
      l <- names(r)
    } else {
      stopifnot(identical(length(l), length(r)))
      which <- l == "?"
      if (any(which)) {
        stopifnot(length(names(r)) > 0)

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#' Get SHAPs from a trained xgboost model
#' @param model trained xgboost model
#' @param data new data for calculating shaps
get_shaps <- function(model, data) {
  p = predict(model, data,
              predcontrib = T, approxcontrib = F)
  p = p[, -ncol(p)]

  shaps = reshape2::melt(p)
  vars = reshape2::melt(data)

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name: Documentation

on:
  push:
    branches:
      - main
      - master
    tags: '*'
  pull_request:

RaphaelS1

library(distr6); library(stats4);library(data.table)

mleEstimate <- function(distr, data, method = "BFGS", fixed = list(), nobs, ...){
  p = as.data.table(distr$parameters())
  p = subset(p, subset = p$settable, select = c("id","value"))

  f = vector("list",length(p$id))
  names(f) = p$id
  LL <- function(){}
  formals(LL) = f