#' Simulated data missing
#'
#' @description Simulated data y=X * beta + epsilon where beta=c(3,2,0,0,1.5,0,...) and with missing values
#'
#' @format this data frame has 10000 rows and the following 50 columns:
#' \describe{
#' \item{y}{the response}
#' \item{V}{Other features}
#' }
"simulated_data_missing"
#' Simulated data additive
#'
#' @description Simulated data y=X * beta + epsilon where beta=c(3,2,0,0,1.5,0,...) and with additive error
#'
#' @format this data frame has 10000 rows and the following 50 columns:
#' \describe{
#' \item{y}{the response}
#' \item{V}{Other features}
#' }
"simulated_data_additive"
#' Simulated data missing block
#'
#' @description Simulated data y=X1 \* beta1 + Z2 \* beta2 + epsilon where beta1=c(3,2,0,0,1.5,0,...)
#' qnd beta2 = c(0,...,1.5,0,0,2,3). X1 is uncorrupted while Z2 is corrupted with missing data
#'
#' @format this data frame has 10000 rows and the following 200 columns:
#' \describe{
#' \item{y}{the response}
#' \item{V}{180 first columns are uncorrupted covariates}
#' \item{V}{20 last columns are the corrupted covariates with missing data}
#' }
"simulated_data_missing_block"
#' Simulated data additive block
#'
#' @description Simulated data y=X1 \* beta1 + Z2 \* beta2 + epsilon where beta1=c(3,2,0,0,1.5,0,...)
#' qnd beta2 = c(0,...,1.5,0,0,2,3). X1 is uncorrupted while Z2 is corrupted with additive error
#'
#' @format this data frame has 10000 rows and the following 200 columns:
#' \describe{
#' \item{y}{the response}
#' \item{V}{180 first columns are uncorrupted covariates}
#' \item{V}{20 last columns are the corrupted covariates with additive error}
#' }
"simulated_data_additive_block"
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