diff --git a/NAMESPACE b/NAMESPACE
index 6cc9e4f7..14f38a8d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -95,6 +95,7 @@ export(util_binomial_param_estimate)
 export(util_binomial_stats_tbl)
 export(util_burr_param_estimate)
 export(util_burr_stats_tbl)
+export(util_cauchy_aic)
 export(util_cauchy_param_estimate)
 export(util_cauchy_stats_tbl)
 export(util_chisq_aic)
diff --git a/R/utils-aic-cauchy.R b/R/utils-aic-cauchy.R
new file mode 100644
index 00000000..095f8791
--- /dev/null
+++ b/R/utils-aic-cauchy.R
@@ -0,0 +1,83 @@
+#' Calculate Akaike Information Criterion (AIC) for Cauchy Distribution
+#'
+#' This function calculates the Akaike Information Criterion (AIC) for a Cauchy
+#' distribution fitted to the provided data.
+#'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the parameters of a Cauchy distribution from the
+#' provided data using maximum likelihood estimation, and then calculates the
+#' AIC value based on the fitted distribution.
+#'
+#' @param .x A numeric vector containing the data to be fitted to a Cauchy
+#' distribution.
+#'
+#' @details
+#' This function fits a Cauchy distribution to the provided data using maximum
+#' likelihood estimation. It first estimates the initial parameters of the
+#' Cauchy distribution using the method of moments. Then, it optimizes the
+#' negative log-likelihood function using the provided data and the initial
+#' parameter estimates. Finally, it calculates the AIC value based on the
+#' fitted distribution.
+#'
+#' Initial parameter estimates: The function uses the method of moments estimates
+#' for the initial location and scale parameters of the Cauchy distribution.
+#'
+#' Optimization method: The function uses the optim function for optimization.
+#' You might explore different optimization methods within optim for potentially
+#' better performance.
+#'
+#' 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 <- rcauchy(30)
+#' util_cauchy_aic(x)
+#'
+#' @return
+#' The AIC value calculated based on the fitted Cauchy distribution to the
+#' provided data.
+#'
+#' @name util_cauchy_aic
+NULL
+
+#' @export
+#' @rdname util_cauchy_aic
+util_cauchy_aic <- function(.x) {
+  # Tidyeval
+  x <- as.numeric(.x)
+
+  # Negative log-likelihood function for Cauchy distribution
+  neg_log_lik_cauchy <- function(par, data) {
+    location <- par[1]
+    scale <- par[2]
+    n <- length(data)
+    -sum(dcauchy(data, location = location, scale = scale, log = TRUE))
+  }
+
+  # Get initial parameter estimates (you might need to adjust this depending on your data)
+  # Here we use method of moments estimates as a starting point
+  pe <- TidyDensity::util_cauchy_param_estimate(x)$parameter_tbl
+
+  # Fit Cauchy distribution using optim
+  fit_cauchy <- optim(
+    c(pe$location, pe$scale),
+    neg_log_lik_cauchy,
+    data = x
+  )
+
+  # Extract log-likelihood and number of parameters
+  logLik_cauchy <- -fit_cauchy$value
+  k_cauchy <- 2 # Number of parameters for Cauchy distribution (location and scale)
+
+  # Calculate AIC
+  AIC_cauchy <- 2 * k_cauchy - 2 * logLik_cauchy
+
+  # Return AIC
+  return(AIC_cauchy)
+}
diff --git a/man/check_duplicate_rows.Rd b/man/check_duplicate_rows.Rd
index 839a8272..02889c77 100644
--- a/man/check_duplicate_rows.Rd
+++ b/man/check_duplicate_rows.Rd
@@ -38,6 +38,7 @@ Other Utility:
 \code{\link{quantile_normalize}()},
 \code{\link{tidy_mcmc_sampling}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()},
 \code{\link{util_normal_aic}()}
 }
diff --git a/man/convert_to_ts.Rd b/man/convert_to_ts.Rd
index a7066c98..b62765ad 100644
--- a/man/convert_to_ts.Rd
+++ b/man/convert_to_ts.Rd
@@ -64,6 +64,7 @@ Other Utility:
 \code{\link{quantile_normalize}()},
 \code{\link{tidy_mcmc_sampling}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()},
 \code{\link{util_normal_aic}()}
 }
diff --git a/man/quantile_normalize.Rd b/man/quantile_normalize.Rd
index 742b4cbc..e4b78563 100644
--- a/man/quantile_normalize.Rd
+++ b/man/quantile_normalize.Rd
@@ -64,6 +64,7 @@ Other Utility:
 \code{\link{convert_to_ts}()},
 \code{\link{tidy_mcmc_sampling}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()},
 \code{\link{util_normal_aic}()}
 }
diff --git a/man/tidy_mcmc_sampling.Rd b/man/tidy_mcmc_sampling.Rd
index 81fee1dd..641d5eac 100644
--- a/man/tidy_mcmc_sampling.Rd
+++ b/man/tidy_mcmc_sampling.Rd
@@ -45,6 +45,7 @@ Other Utility:
 \code{\link{convert_to_ts}()},
 \code{\link{quantile_normalize}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()},
 \code{\link{util_normal_aic}()}
 }
diff --git a/man/util_beta_aic.Rd b/man/util_beta_aic.Rd
index 2bab7eaa..3d8b2580 100644
--- a/man/util_beta_aic.Rd
+++ b/man/util_beta_aic.Rd
@@ -47,6 +47,7 @@ Other Utility:
 \code{\link{convert_to_ts}()},
 \code{\link{quantile_normalize}()},
 \code{\link{tidy_mcmc_sampling}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()},
 \code{\link{util_normal_aic}()}
 }
diff --git a/man/util_cauchy_aic.Rd b/man/util_cauchy_aic.Rd
new file mode 100644
index 00000000..cdeb578f
--- /dev/null
+++ b/man/util_cauchy_aic.Rd
@@ -0,0 +1,64 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils-aic-cauchy.R
+\name{util_cauchy_aic}
+\alias{util_cauchy_aic}
+\title{Calculate Akaike Information Criterion (AIC) for Cauchy Distribution}
+\usage{
+util_cauchy_aic(.x)
+}
+\arguments{
+\item{.x}{A numeric vector containing the data to be fitted to a Cauchy
+distribution.}
+}
+\value{
+The AIC value calculated based on the fitted Cauchy distribution to the
+provided data.
+}
+\description{
+This function estimates the parameters of a Cauchy distribution from the
+provided data using maximum likelihood estimation, and then calculates the
+AIC value based on the fitted distribution.
+}
+\details{
+This function calculates the Akaike Information Criterion (AIC) for a Cauchy
+distribution fitted to the provided data.
+
+This function fits a Cauchy distribution to the provided data using maximum
+likelihood estimation. It first estimates the initial parameters of the
+Cauchy distribution using the method of moments. Then, it optimizes the
+negative log-likelihood function using the provided data and the initial
+parameter estimates. Finally, it calculates the AIC value based on the
+fitted distribution.
+
+Initial parameter estimates: The function uses the method of moments estimates
+for the initial location and scale parameters of the Cauchy distribution.
+
+Optimization method: The function uses the optim function for optimization.
+You might explore different optimization methods within optim for potentially
+better performance.
+
+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 <- rcauchy(30)
+util_cauchy_aic(x)
+
+}
+\seealso{
+Other Utility: 
+\code{\link{check_duplicate_rows}()},
+\code{\link{convert_to_ts}()},
+\code{\link{quantile_normalize}()},
+\code{\link{tidy_mcmc_sampling}()},
+\code{\link{util_beta_aic}()},
+\code{\link{util_chisq_aic}()},
+\code{\link{util_normal_aic}()}
+}
+\author{
+Steven P. Sanderson II, MPH
+}
+\concept{Utility}
diff --git a/man/util_chisq_aic.Rd b/man/util_chisq_aic.Rd
index ee39cb97..8496c202 100644
--- a/man/util_chisq_aic.Rd
+++ b/man/util_chisq_aic.Rd
@@ -32,6 +32,7 @@ Other Utility:
 \code{\link{quantile_normalize}()},
 \code{\link{tidy_mcmc_sampling}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_normal_aic}()}
 }
 \author{
diff --git a/man/util_normal_aic.Rd b/man/util_normal_aic.Rd
index 9ba23758..01e00694 100644
--- a/man/util_normal_aic.Rd
+++ b/man/util_normal_aic.Rd
@@ -33,6 +33,7 @@ Other Utility:
 \code{\link{quantile_normalize}()},
 \code{\link{tidy_mcmc_sampling}()},
 \code{\link{util_beta_aic}()},
+\code{\link{util_cauchy_aic}()},
 \code{\link{util_chisq_aic}()}
 }
 \author{