#' NeuralNetworkVisualization: Create Partial Dependence Plots for Neural Networks.
#'
#' NeuralNetworkVisualization is a R Package for creating beautiful partial
#' dependence plots for Neural Networks using ggplot2, plotly and shiny.
#' It is possible to add confidence intervals created with a bootstrap
#' procedure. There are three prefitted models that can be used for testing.
#' You can acess them with the example_nn_model function.
#'
#' @docType package
#' @name NeuralNetworkVisualization
NULL
#' Returns example models by type of response
#'
#' Each model has stored data available with 1000 bootstrap iterations and a
#' 90\% confidence interval. The code that fitted the models can be found here:
#' \url{https://github.com/AlexAfanasev/NeuralNetworkVisualization/blob/master/inst/examples.R}
#'
#' @return NeuralNetwork class
#'
#' @examples
#' \dontrun{
#' # Example: Numeric
#' example_nn_model("numerical")
#'
#' # Example: Categoric
#' example_nn_model("categorical")
#'
#' Example: Binary
#' example_nn_model("binary")
#'
#' }
#'
#' @param type String beeing either numerical, categorical or binary.
#'
#' @name example_nn_model
#' @export
example_nn_model <- function(type){
if (type %in% c("categorical", "binary", "numerical")) {
model_file_directory <- system.file(
"models", package = "NeuralNetworkVisualization")
model_file <- list.files(model_file_directory, full.names = TRUE)[
which(list.files(model_file_directory) == paste(
type, ".rds", sep = ""))]
model <- readRDS(file = model_file)
return(model)
} else {
stop("Please specify either numerical, categorical or binary!")
}
}
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