Merge pull request #448 from spsanderson/development

Fixes #431

NAMESPACE

@@ -131,6 +131,7 @@ export(util_poisson_stats_tbl)
 export(util_t_stats_tbl)
 export(util_triangular_param_estimate)
 export(util_triangular_stats_tbl)
+export(util_uniform_aic)
 export(util_uniform_param_estimate)
 export(util_uniform_stats_tbl)
 export(util_weibull_param_estimate)

R/utils-aic-uniform.R

@@ -0,0 +1,61 @@
+#' Calculate Akaike Information Criterion (AIC) for Uniform Distribution
+#'
+#' This function calculates the Akaike Information Criterion (AIC) for a uniform
+#' distribution fitted to the provided data.
+#'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the min and max parameters of a uniform distribution
+#' from the provided data and then calculates the AIC value based on the fitted
+#' distribution.
+#'
+#' @param .x A numeric vector containing the data to be fitted to a uniform distribution.
+#'
+#' @details
+#' This function fits a uniform distribution to the provided data. It estimates
+#' the min and max parameters of the uniform distribution from the range of the data.
+#' Then, it calculates the AIC value based on the fitted distribution.
+#'
+#' Initial parameter estimates: The function uses the minimum and maximum values
+#' of the data as starting points for the min and max parameters of the uniform
+#' distribution.
+#'
+#' Optimization method: Since the parameters are directly calculated from the
+#' data, no optimization is needed.
+#'
+#' Goodness-of-fit: While AIC is a useful metric for model comparison,
+#' it's recommended to also assess the goodness-of-fit of the chosen
+#' model using visualization and other statistical tests.
+#'
+#' @examples
+#' # Example 1: Calculate AIC for a sample dataset
+#' set.seed(123)
+#' x <- runif(30)
+#' util_uniform_aic(x)
+#'
+#' @return
+#' The AIC value calculated based on the fitted uniform distribution to the provided data.
+#'
+#' @name util_uniform_aic
+NULL
+
+#' @export
+#' @rdname util_uniform_aic
+util_uniform_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Estimate min and max parameters
+  min_val <- min(x)
+  max_val <- max(x)
+
+  # Calculate AIC
+  k_uniform <- 2 # Number of parameters for uniform distribution (min and max)
+  logLik_uniform <- -length(x) * log(max_val - min_val)
+  AIC_uniform <- 2 * k_uniform - 2 * logLik_uniform
+
+  # Return AIC
+  return(AIC_uniform)
+}