#' Get the left and right Median Absolute Deviations (MAD) from the median for asymmetric distributions

#'

#' Suited to find outliers in asymetric distributions (in contrast to the standard mad() function which works for symmetric distributions only)

#' The function splits the values along the median and returns separate MADs for the left and the right side of the distribution.

#' https://eurekastatistics.com/using-the-median-absolute-deviation-to-find-outliers/

#'

#' @param x A vector of numeric values.

#' @param zero_mad_action Determines the action in the event of an MAD of zero.

#' Defaults to NULL. The options are:

#' * \strong{NULL}: process runs with no warning

#' * \strong{"warn"}: a warning will be displayed

#' * \strong{"stop"}: process is stopped

#'

#' @return A numeric vector of length 2.

#'

#' @importFrom stats median

#'

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' double_mad(x)

#'

double_mad <- function(x, zero_mad_action = NULL){

}

#' Calculate the distance of a value from the median of a distribution in relation to its Median Absolute Deviation (MAD)

#'

#' This function is suited to find outliers in asymetric distributions (in contrast to the standard mad() function which works for

#' symetric distributions only). The function splits the values along the median and returns the distance for every value from the

#' median, relative to the left or right side MAD.

#' https://eurekastatistics.com/using-the-median-absolute-deviation-to-find-outliers/

#'

#' @inheritParams double_mad

#'

#' @importFrom stats median

#'

#' @return A numeric vector of length \code{length(x)}.

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' double_mad_from_median(x)

#'

double_mad_from_median <- function(x, zero_mad_action = NULL){

}

#' Detect outliers using MAD from the median for asymmetric distributions

#'

#' Outlier detection based on Median Absolute Deviation (MAD) for asymmetric distributions. The function calculates the distance

#' to the median for every value in the distribution relative to the left or right side MAD. It then compares the value to your

#' threshold and labels the outliers.

#'

#' @inheritParams double_mad

#' @param threshold Z-score threshold (defaults to 3.5).

#'

#' @return A logical vector.

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' is_outlier_double_mad(x)

#'

is_outlier_double_mad <- function(x, zero_mad_action = NULL, threshold = 3.5){

}

#' Get boundaries beyond which a value is an outlier via MAD from the median for asymmetric distributions and IQR

#'

#' Outlier detection based on Median Absolute Deviation (MAD) for asymetric distributions and interquartile range. The function

#' calculates the distance to the median for every value in the distribution relative to the left or right side MAD. It then

#' compares the value to your threshold and labels the outliers.

#'

#' @inheritParams is_outlier_double_mad

#' @param percent Indicator for the scale of the data. If function is run for percantage data, the lower limit will not be negative

#' while the upper limit does not exceed 100 percent. Defaults to TRUE.

#'

#' @importFrom stats median

#' @importFrom stats IQR

#'

#' @return A data.frame with numeric range.

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' outlier_range(x)

#'

outlier_range <- function(x, zero_mad_action = NULL, threshold = 3.5, percent = TRUE){

}

#' Detect outliers using Z-score with MAD for symmetric distributions

#'

#' Outlier detection based on Median Absolute Deviation (MAD) for symmetric distributions. The function calculates the distance

#' to the median for every value in the distribution relative to the MAD. It then compares the value to your threshold and

#' labels the outliers.

#'

#'

#' @inheritParams double_mad

#' @param threshold Z-score threshold (defaults to 3).

#' @param na.rm Remove NAs, defaults to TRUE.

#'

#' @importFrom stats median

#' @importFrom stats mad

#'

#' @return A logical vector.

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' is_outlier_single_mad(x)

#'

is_outlier_single_mad <- function(x, threshold = 3, na.rm = TRUE) {

}

#' Detect outliers using classic Z-scores for symmetric distributions

#'

#' Outlier detection based on Z-scores for symetric distributions. The function calculates the Z-score, i. e. the distance of a value

#' from the mean in number of standard deviations.

#'

#'

#' @inheritParams is_outlier_single_mad

#'

#' @importFrom stats sd

#'

#' @return A logical vector.

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' is_outlier_z(x)

#'

is_outlier_z <- function(x, threshold = 3, na.rm = TRUE) {

}

#' Detect outliers using turkey's fences

#'

#' Outlier detection based on turkey's fences. Tukey’s fences is a technique used in box plots. The non-outlier range is defined as

#' Q1−k(Q3−Q1), Q3+k(Q3−Q1), where Q1 and Q3 are the lower and upper quartiles respectively and k - some non-negative constant

#' (popular choice is 1.5).

#'

#'

#' @inheritParams double_mad

#' @param threshold Multiplier for the IQR to set outlier boundaries. Higher values widen the range; default is 1.5.

#' @param na.rm if TRUE, removes NA values before calculations. Default is TRUE.

#'

#' @importFrom stats quantile

#'

#' @return A logical vector.

#'

#' @export

#'

#' @examples

#'

#' x <- c(1, 2, 3, 3, 4, 4, 4, 5, 5.5, 6, 6, 6.5, 7, 7, 7.5, 8, 9, 12, 52, 90)

#'

#' is_outlier_turkey(x)

#'

is_outlier_turkey <- function(x, threshold = 1.5, na.rm = TRUE) {

}