Merge pull request #491 from spsanderson/development

update site
spsanderson · May 9, 2024 · b589b9c · b589b9c
2 parents 3f1fa87 + 405f1a3
commit b589b9c
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diff --git a/NAMESPACE b/NAMESPACE
@@ -135,7 +135,6 @@ export(util_pareto_stats_tbl)
 export(util_poisson_aic)
 export(util_poisson_param_estimate)
 export(util_poisson_stats_tbl)
-export(util_rztnbinom_aic)
 export(util_t_stats_tbl)
 export(util_triangular_param_estimate)
 export(util_triangular_stats_tbl)
@@ -145,11 +144,12 @@ export(util_uniform_stats_tbl)
 export(util_weibull_aic)
 export(util_weibull_param_estimate)
 export(util_weibull_stats_tbl)
+export(util_zero_truncated_negative_binomial_aic)
+export(util_zero_truncated_negative_binomial_param_estimate)
+export(util_zero_truncated_negative_binomial_stats_tbl)
 export(util_zero_truncated_poisson_aic)
 export(util_zero_truncated_poisson_param_estimate)
 export(util_zero_truncated_poisson_stats_tbl)
-export(util_ztn_binomial_param_estimate)
-export(util_ztn_binomial_stats_tbl)
 importFrom(data.table,.SD)
 importFrom(data.table,as.data.table)
 importFrom(data.table,melt)

diff --git a/NEWS.md b/NEWS.md
@@ -5,9 +5,14 @@ None
 
 ## New Features
 1. #468 - Add function `util_negative_binomial_aic()` to calculate the AIC for the negative binomial distribution.
-2. #470 - Add function `util_ztn_binomial_param_estimate()` and `util_rztnbinom_aic()` to estimate the parameters and calculate the AIC for the zero-truncated negative binomial distribution. Also added `util_ztn_binomial_stats_tbl()`
+2. #470 - Add function `util_zero_truncated_negative_binomial_param_estimate()` to
+estimate the parameters of the zero-truncated negative binomial distribution.
+Add function `util_zero_truncated_negative_binomial_aic()` to calculate the AIC for the zero-truncated negative binomial distribution.
+Add function `util_zero_truncated_negative_binomial_stats_tbl()` to create a summary table of the zero-truncated negative binomial distribution.
 3. #471 - Add function `util_zero_truncated_poisson_param_estimate()` to estimate
-the parameters of the zero-truncated Poisson distribution. Add function `util_zero_truncated_poisson_aic()` to calculate the AIC for the zero-truncated Poisson distribution. Add function `util_zero_truncated_poisson_stats_tbl()` to create a summary table of the zero-truncated Poisson distribution.
+the parameters of the zero-truncated Poisson distribution. 
+Add function `util_zero_truncated_poisson_aic()` to calculate the AIC for the zero-truncated Poisson distribution. 
+Add function `util_zero_truncated_poisson_stats_tbl()` to create a summary table of the zero-truncated Poisson distribution.
 4. #472 - Add function `util_f_param_estimate()` and `util_f_aic()` to estimate the parameters and calculate the AIC for the F distribution.
 
 ## Minor Improvements and Fixes

diff --git a/R/est-param-negative-binomial.R b/R/est-param-negative-binomial.R
@@ -112,11 +112,11 @@ util_negative_binomial_param_estimate <- function(.x, .size = 1,
   nll_func <- function(params) {
     size <- params[1]
     mu <- params[2]
-    -sum(dnbinom(x, size = size, mu = mu, log = TRUE))
+    -sum(dnbinom(x_term, size = size, mu = mu, log = TRUE))
   }
 
   # Initial parameter guesses (you might need to adjust these based on your data)
-  initial_params <- c(size = 1, mu = mean(x))
+  initial_params <- c(size = 1, mu = mean(x_term))
 
   # Optimize using optim()
   optim_result <- optim(initial_params, nll_func)

diff --git a/R/est-param-ztn-binmoial.R b/R/est-param-ztn-binmoial.R
@@ -29,7 +29,7 @@
 #' library(actuar)
 #'
 #' x <- as.integer(mtcars$mpg)
-#' output <- util_ztn_binomial_param_estimate(x)
+#' output <- util_zero_truncated_negative_binomial_param_estimate(x)
 #'
 #' output$parameter_tbl
 #'
@@ -38,15 +38,19 @@
 #'
 #' set.seed(123)
 #' t <- rztnbinom(100, 10, .1)
-#' util_ztn_binomial_param_estimate(t)$parameter_tbl
+#' util_zero_truncated_negative_binomial_param_estimate(t)$parameter_tbl
 #'
+#' @name util_zero_truncated_negative_binomial_param_estimate
+NULL
+
 #' @return
 #' A tibble/list
 #'
+#' @rdname util_zero_truncated_negative_binomial_param_estimate
 #' @export
 #'
 
-util_ztn_binomial_param_estimate <- function(.x, .auto_gen_empirical = TRUE) {
+util_zero_truncated_negative_binomial_param_estimate <- function(.x, .auto_gen_empirical = TRUE) {
 
   # Check if actuar library is installed
   if (!requireNamespace("actuar", quietly = TRUE)) {

diff --git a/R/est-param-ztpois.R b/R/est-param-ztpois.R
@@ -85,7 +85,7 @@ util_zero_truncated_poisson_param_estimate <- function(.x, .auto_gen_empirical =
   # Optimize to find lambda that minimizes negative log-likelihood
   optim_result <- stats::optim(par = 1, fn = neg_loglik, data = x_term,
                                method = "Brent",
-                               lower = 0, upper = max(x))
+                               lower = 0, upper = max(x_term))
 
   # Extract estimated lambda
   lambda_est <- optim_result$par
@@ -109,7 +109,7 @@ util_zero_truncated_poisson_param_estimate <- function(.x, .auto_gen_empirical =
   # Return ----
   attr(ret, "tibble_type") <- "parameter_estimation"
   attr(ret, "family") <- "zero truncated poisson"
-  attr(ret, "x_term") <- .x
+  attr(ret, "x_term") <- x_term
   attr(ret, "n") <- n
 
   if (.auto_gen_empirical) {

diff --git a/R/stats-ztn-binomial-tbl.R b/R/stats-ztn-binomial-tbl.R
@@ -18,19 +18,19 @@
 #' library(dplyr)
 #'
 #' tidy_zero_truncated_negative_binomial(.size = 1, .prob = 0.1) |>
-#'  util_ztn_binomial_stats_tbl() |>
+#'  util_zero_truncated_negative_binomial_stats_tbl() |>
 #'  glimpse()
 #'
 #'
 #' @return A tibble with distribution statistics.
 #'
-#' @name util_ztn_binomial_stats_tbl
+#' @name util_zero_truncated_negative_binomial_stats_tbl
 NULL
 
 #' @export
-#' @rdname util_ztn_binomial_stats_tbl
+#' @rdname util_zero_truncated_negative_binomial_stats_tbl
 
-util_ztn_binomial_stats_tbl <- function(.data) {
+util_zero_truncated_negative_binomial_stats_tbl <- function(.data) {
 
   # Immediate check for tidy_ distribution function
   if (!"tibble_type" %in% names(attributes(.data))) {

diff --git a/R/utils-aic-f.R b/R/utils-aic-f.R
@@ -3,6 +3,14 @@
 #' This function calculates the Akaike Information Criterion (AIC) for an F
 #' distribution fitted to the provided data.
 #'
+#' @family Utility
+#' @author Steven P. Sanderson II, MPH
+#'
+#' @description
+#' This function estimates the parameters of a F 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 an F
 #' distribution.
 #'
@@ -34,10 +42,10 @@ NULL
 
 util_f_aic <- function(.x) {
   # Tidyeval
-  x <- as.numeric(.x)
+  x_term <- as.numeric(.x)
 
   # Get initial parameter estimates
-  pe <- TidyDensity::util_f_param_estimate(x)$parameter_tbl |>
+  pe <- TidyDensity::util_f_param_estimate(x_term)$parameter_tbl |>
     dplyr::filter(method == "MLE")
 
   # Negative log-likelihood function for the F-distribution

diff --git a/R/utils-aic-ztn-binomial.R b/R/utils-aic-ztn-binomial.R
@@ -43,18 +43,18 @@
 #' x <- rztnbinom(30, size = 2, prob = 0.4)
 #'
 #' # Calculate AIC
-#' util_rztnbinom_aic(x)
+#' util_zero_truncated_negative_binomial_aic(x)
 #'
 #' @return The AIC value calculated based on the fitted ZTNB distribution to
 #'   the provided data.
 #'
-#' @name util_rztnbinom_aic
+#' @name util_zero_truncated_negative_binomial_aic
 NULL
 
 #' @export
-#' @rdname util_rztnbinom_aic
+#' @rdname util_zero_truncated_negative_binomial_aic
 
-util_rztnbinom_aic <- function(.x) {
+util_zero_truncated_negative_binomial_aic <- function(.x) {
   # Check if actuar library is installed
   if (!requireNamespace("actuar", quietly = TRUE)) {
     stop("The 'actuar' package is needed for this function. Please install it with: install.packages('actuar')")
@@ -64,7 +64,7 @@ util_rztnbinom_aic <- function(.x) {
   x <- as.numeric(.x)
 
   # Get parameters
-  pe <- util_ztn_binomial_param_estimate(x)$parameter_tbl
+  pe <- TidyDensity::util_zero_truncated_negative_binomial_param_estimate(x)$parameter_tbl
 
   # Negative log-likelihood function for zero-truncated negative binomial distribution
   neg_log_lik_rztnbinom <- function(par, data) {

diff --git a/docs/news/index.html b/docs/news/index.html
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
@@ -1,9 +1,9 @@
-pandoc: 3.1.1
+pandoc: 3.1.11
 pkgdown: 2.0.9
 pkgdown_sha: ~
 articles:
   getting-started: getting-started.html
-last_built: 2024-04-30T17:59Z
+last_built: 2024-05-09T15:29Z
 urls:
   reference: https://www.spsanderson.com/TidyDensity/reference
   article: https://www.spsanderson.com/TidyDensity/articles

diff --git a/docs/reference/check_duplicate_rows.html b/docs/reference/check_duplicate_rows.html
diff --git a/docs/reference/convert_to_ts.html b/docs/reference/convert_to_ts.html
diff --git a/docs/reference/index.html b/docs/reference/index.html