#' @include mungebit_template.R
NULL
#' remove outliers based upon a z_score threshold z_score=> (x - mean(x) / sd(x)
#'
#' @name remove_outliers
#' @param x an numeric vector
#' @param threshold a z_score threshold from which the absolute value of a z_score above this threshold will be set to NA
#' @export
NULL
remove_outliers <- mungebit_template({
column_transformation <- TRUE
train <- predict <- function(x, threshold = 3) {
if (!is.element("removed_outliers", names(input))) {
input$mean <- mean(x, na.rm = TRUE)
input$sd <- sd(x, na.rm = TRUE)
input$removed_outliers <- TRUE # not sure if another mungebit has a mean namespace
}
x[abs((x - input$mean) / input$sd) > threshold] <- NA
x
}
})
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