pacotes <- c("dplyr","openxlsx","readxl","topsis")

if(sum(as.numeric(!pacotes %in% installed.packages())) != 0){
  instalador <- pacotes[!pacotes %in% installed.packages()]
  for(i in 1:length(instalador)) {
    install.packages(instalador, dependencies = T)
    break()}
  sapply(pacotes, require, character = T)
} else {
  sapply(pacotes, require, character = T)
}

' @title pmtcR
#'
#' @description Receives a standardized dataset and returns the ranking of the
#' alternatives according to the user-defined criteria weight calculation method
#' and the user-defined ranking method.
#'
#' @param df A dataset object
#' @param weight_method Method for calculating weights or user-defined weights
#' @param ranking_method Method for ranking the alternatives
#' @param alpha Parameter alpha for the CoCoSo method (optional, used only for
#' CoCoSo method)
#'
#' @return A dataframe containing the ranked alternatives and the ranking
#' metrics according to the chosen method.
#'
#' @examples
#' # Example usage:
#' # df <- df4
#' # result <- pmtcR(df,ranking_method = "cocoso")
#'
#' @export
#' @import dplyr
#' @importFrom openxlsx read.xlsx
#' @importFrom readxl read_excel
#' @importFrom topsis topsis

pmtcR <- function(df, weight_method = "decisor", ranking_method, alpha = 0.5) {
  alternatives <- df[3:nrow(df), 1]
  alternatives <- as.data.frame(alternatives)
  colnames(alternatives) <- "Alternatives"
  weights_decisor <- as.numeric(df[2, 2:ncol(df)])
  criteria_direction <- df[1, 2:ncol(df)]
  criterias <- colnames(df)[2:ncol(df)]
  matrix_decis <- as.data.frame(df[3:nrow(df), 2:ncol(df)])
  colnames(matrix_decis) <- criterias
  matrix_decis <- matrix_decis %>% mutate_all(as.numeric)

  if (weight_method == "decisor" & abs(sum(weights_decisor) - 1) > 1E-10) {
    stop("The sum of the weights must be equal to 1!")
  }
  if (any(!sapply(matrix_decis, is.numeric))) {
    stop("All values in the Decision Matrix must be numeric!")
  }

  if (any(is.na(alternatives))) {
    stop("All alternatives must be named!")
  }

  if (any(is.na(criterias))) {
    stop("All criteria must be named!")
  }

  if (any(!criteria_direction %in% c("MAX", "MIN"))) {
    stop("All criteria must be either MAXIMIZATION or MINIMIZATION!")
  }

  if (!weight_method %in% c("psi", "mpsi", "topsis", "decisor")) {
    stop("Choose a valid elicitation method for the weights!")
  }

  if (!ranking_method %in% c("psi", "topsis", "cocoso")) {
    stop("Choose a valid ranking method!")
  }

  # IF ELSE for the weights ****************************************************
  if(weight_method == "psi") {
    vector_weights <- weights_psi(df)
  } else if(weight_method == "mpsi") {
    vector_weights <- weights_mpsi(df)
  } else {
    vector_weights <- as.numeric(df[2, 2:ncol(df)])
  }

  # IF ELSE for the ranking method *********************************************
  if(ranking_method == "psi") {
    ranked_alternatives <- rank_psi(df, vector_weights)
  } else if(ranking_method == "topsis") {
    ranked_alternatives <- rank_topsis(df, vector_weights)
  } else {
    ranked_alternatives <- rank_cocoso(df, vector_weights, alpha)
  }
  return(ranked_alternatives)
}

# ranking <- pmtcR(df4,ranking_method = "cocoso")
# View(ranking)