diff --git a/DESCRIPTION b/DESCRIPTION
index d79f7589..7ef4046f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -18,6 +18,8 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
 URL: https://github.com/spsanderson/TidyDensity
 BugReports: https://github.com/spsanderson/TidyDensity/issues
+Depends:
+    R (>= 4.1.0)
 Imports: 
     magrittr,
     rlang (>= 0.4.11),
diff --git a/R/augment-bootstrap-density.R b/R/augment-bootstrap-density.R
index 824972e4..52856fca 100644
--- a/R/augment-bootstrap-density.R
+++ b/R/augment-bootstrap-density.R
@@ -22,11 +22,11 @@
 #' @examples
 #' x <- mtcars$mpg
 #'
-#' tidy_bootstrap(x) %>%
+#' tidy_bootstrap(x) |>
 #'   bootstrap_density_augment()
 #'
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_density_augment()
 #'
 #' @return
@@ -56,7 +56,7 @@ bootstrap_density_augment <- function(.data) {
 
   # Add density data
   if (atb$tibble_type == "tidy_bootstrap_nested") {
-    df_tbl <- dplyr::as_tibble(.data) %>%
+    df_tbl <- dplyr::as_tibble(.data) |>
       TidyDensity::bootstrap_unnest_tbl()
   }
 
@@ -64,18 +64,18 @@ bootstrap_density_augment <- function(.data) {
     df_tbl <- dplyr::as_tibble(.data)
   }
 
-  df_tbl <- df_tbl %>%
-    dplyr::nest_by(sim_number) %>%
+  df_tbl <- df_tbl |>
+    dplyr::nest_by(sim_number) |>
     dplyr::mutate(dens_tbl = list(
       stats::density(unlist(data),
         n = nrow(data)
-      )[c("x", "y")] %>%
-        purrr::set_names("dx", "dy") %>%
+      )[c("x", "y")] |>
+        purrr::set_names("dx", "dy") |>
         dplyr::as_tibble()
-    )) %>%
-    tidyr::unnest(cols = c(data, dens_tbl)) %>%
-    dplyr::mutate(x = dplyr::row_number()) %>%
-    dplyr::ungroup() %>%
+    )) |>
+    tidyr::unnest(cols = c(data, dens_tbl)) |>
+    dplyr::mutate(x = dplyr::row_number()) |>
+    dplyr::ungroup() |>
     dplyr::select(sim_number, x, y, dx, dy, dplyr::everything())
 
   # Return
diff --git a/R/augment-bootstrap-p.R b/R/augment-bootstrap-p.R
index f6ad8845..aa778a1f 100644
--- a/R/augment-bootstrap-p.R
+++ b/R/augment-bootstrap-p.R
@@ -20,8 +20,8 @@
 #'
 #' @examples
 #' x <- mtcars$mpg
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_p_augment(y)
 #'
 #' @return
diff --git a/R/augment-bootstrap-q.R b/R/augment-bootstrap-q.R
index d82139be..e03a05bb 100644
--- a/R/augment-bootstrap-q.R
+++ b/R/augment-bootstrap-q.R
@@ -21,8 +21,8 @@
 #' @examples
 #' x <- mtcars$mpg
 #'
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_q_augment(y)
 #'
 #' @return
diff --git a/R/autoplot-combined-dist.R b/R/autoplot-combined-dist.R
index ebf5fc0a..4bfe254b 100644
--- a/R/autoplot-combined-dist.R
+++ b/R/autoplot-combined-dist.R
@@ -47,10 +47,10 @@
 #'
 #' combined_tbl
 #'
-#' combined_tbl %>%
+#' combined_tbl |>
 #'   tidy_combined_autoplot()
 #'
-#' combined_tbl %>%
+#' combined_tbl |>
 #'   tidy_combined_autoplot(.plot_type = "qq")
 #'
 #' @return
@@ -126,7 +126,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
   }
 
   if (plot_type == "density") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = dx, y = dy,
@@ -144,16 +144,16 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "quantile") {
     ## EDIT
-    data_tbl <- data_tbl %>%
-      dplyr::select(sim_number, dist_type, q) %>%
-      dplyr::group_by(sim_number, dist_type) %>%
-      dplyr::arrange(q) %>%
-      dplyr::mutate(x = 1:dplyr::n() %>%
-        tidy_scale_zero_one_vec()) %>%
+    data_tbl <- data_tbl |>
+      dplyr::select(sim_number, dist_type, q) |>
+      dplyr::group_by(sim_number, dist_type) |>
+      dplyr::arrange(q) |>
+      dplyr::mutate(x = 1:dplyr::n() |>
+        tidy_scale_zero_one_vec()) |>
       dplyr::ungroup()
     ## End EDIT
-    plt <- data_tbl %>%
-      dplyr::filter(q > -Inf, q < Inf) %>%
+    plt <- data_tbl |>
+      dplyr::filter(q > -Inf, q < Inf) |>
       ggplot2::ggplot(
         ggplot2::aes(
           # x = tidy_scale_zero_one_vec(dx),
@@ -174,7 +174,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "probability") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = y,
@@ -192,7 +192,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "qq") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           sample = y,
@@ -210,10 +210,10 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "mcmc") {
-    plt <- data_tbl %>%
-      dplyr::group_by(sim_number, dist_type) %>%
-      dplyr::mutate(cmy = dplyr::cummean(y)) %>%
-      dplyr::ungroup() %>%
+    plt <- data_tbl |>
+      dplyr::group_by(sim_number, dist_type) |>
+      dplyr::mutate(cmy = dplyr::cummean(y)) |>
+      dplyr::ungroup() |>
       ggplot2::ggplot(ggplot2::aes(
         x = x, y = cmy, group = interaction(dist_type, sim_number), color = sim_number
       )) +
diff --git a/R/autoplot-four-graphs.R b/R/autoplot-four-graphs.R
index 37b64471..16074955 100644
--- a/R/autoplot-four-graphs.R
+++ b/R/autoplot-four-graphs.R
@@ -39,7 +39,7 @@
 #' will return an interactive `plotly` plot.
 #'
 #' @examples
-#' tidy_normal(.num_sims = 5) %>%
+#' tidy_normal(.num_sims = 5) |>
 #'   tidy_four_autoplot()
 #'
 #' @return
diff --git a/R/autoplot-multi-dist.R b/R/autoplot-multi-dist.R
index 909e9002..8d9e9a56 100644
--- a/R/autoplot-multi-dist.R
+++ b/R/autoplot-multi-dist.R
@@ -50,10 +50,10 @@
 #'   )
 #' )
 #'
-#' tn %>%
+#' tn |>
 #'   tidy_multi_dist_autoplot()
 #'
-#' tn %>%
+#' tn |>
 #'   tidy_multi_dist_autoplot(.plot_type = "qq")
 #'
 #' @return
@@ -223,7 +223,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
   }
 
   if (plot_type == "density" & atb$all$distribution_family_type == "continuous") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(x = dx, y = dy, group = interaction(dist_name, sim_number), color = dist_name)
       ) +
@@ -236,7 +236,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "density" & atb$all$distribution_family_type == "discrete") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(x = y, group = interaction(dist_name, sim_number), fill = dist_name)
       ) +
@@ -253,14 +253,14 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "quantile") {
     ## EDIT
-    data_tbl <- data_tbl %>%
-      dplyr::select(sim_number, dist_name, q) %>%
-      dplyr::group_by(sim_number, dist_name) %>%
-      dplyr::arrange(q) %>%
-      dplyr::mutate(x = 1:dplyr::n()) %>%
+    data_tbl <- data_tbl |>
+      dplyr::select(sim_number, dist_name, q) |>
+      dplyr::group_by(sim_number, dist_name) |>
+      dplyr::arrange(q) |>
+      dplyr::mutate(x = 1:dplyr::n()) |>
       dplyr::ungroup()
     ## End EDIT
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = x, y = q, group = interaction(dist_name, sim_number), color = dist_name
@@ -275,7 +275,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "probability") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = y, group = interaction(dist_name, sim_number), color = dist_name
@@ -290,7 +290,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "qq") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           sample = y, group = interaction(dist_name, sim_number), color = dist_name
@@ -306,10 +306,10 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "mcmc") {
-    plt <- data_tbl %>%
-      dplyr::group_by(sim_number, dist_name) %>%
-      dplyr::mutate(cmy = dplyr::cummean(y)) %>%
-      dplyr::ungroup() %>%
+    plt <- data_tbl |>
+      dplyr::group_by(sim_number, dist_name) |>
+      dplyr::mutate(cmy = dplyr::cummean(y)) |>
+      dplyr::ungroup() |>
       ggplot2::ggplot(ggplot2::aes(
         x = x, y = cmy, group = interaction(dist_name, sim_number), color = sim_number
       )) +
diff --git a/R/autoplot-randomwalk.R b/R/autoplot-randomwalk.R
index 3e0834a1..95030194 100644
--- a/R/autoplot-randomwalk.R
+++ b/R/autoplot-randomwalk.R
@@ -27,12 +27,12 @@
 #' will return an interactive `plotly` plot.
 #'
 #' @examples
-#' tidy_normal(.sd = .1, .num_sims = 5) %>%
-#'   tidy_random_walk(.value_type = "cum_sum") %>%
+#' tidy_normal(.sd = .1, .num_sims = 5) |>
+#'   tidy_random_walk(.value_type = "cum_sum") |>
 #'   tidy_random_walk_autoplot()
 #'
-#' tidy_normal(.sd = .1, .num_sims = 20) %>%
-#'   tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) %>%
+#' tidy_normal(.sd = .1, .num_sims = 20) |>
+#'   tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |>
 #'   tidy_random_walk_autoplot()
 #'
 #' @return
@@ -51,8 +51,8 @@ tidy_random_walk_autoplot <- function(.data, .line_size = .5, .geom_rug = FALSE,
   atb <- attributes(.data)
   n <- atb$all$.n
   sims <- atb$all$.num_sims
-  # dist_type <- stringr::str_remove(atb$all$tibble_type, "tidy_") %>%
-  #   stringr::str_replace_all(pattern = "_", " ") %>%
+  # dist_type <- stringr::str_remove(atb$all$tibble_type, "tidy_") |>
+  #   stringr::str_replace_all(pattern = "_", " ") |>
   #   stringr::str_to_title()
   dist_type <- dist_type_extractor(atb$all$tibble_type)
 
@@ -160,7 +160,7 @@ tidy_random_walk_autoplot <- function(.data, .line_size = .5, .geom_rug = FALSE,
   data_tbl <- dplyr::as_tibble(.data)
 
   # Plot ----
-  plt <- data_tbl %>%
+  plt <- data_tbl |>
     ggplot2::ggplot(ggplot2::aes(
       x = x, y = random_walk_value,
       group = sim_number, color = sim_number
diff --git a/R/bootstrap-unnest-tbl.R b/R/bootstrap-unnest-tbl.R
index 7b67e794..be4d6458 100644
--- a/R/bootstrap-unnest-tbl.R
+++ b/R/bootstrap-unnest-tbl.R
@@ -18,7 +18,7 @@
 #' tb <- tidy_bootstrap(.x = mtcars$mpg)
 #' bootstrap_unnest_tbl(tb)
 #'
-#' bootstrap_unnest_tbl(tb) %>%
+#' bootstrap_unnest_tbl(tb) |>
 #'   tidy_distribution_summary_tbl(sim_number)
 #'
 #' @return
@@ -41,7 +41,7 @@ bootstrap_unnest_tbl <- function(.data) {
   }
 
   # Data ----
-  df <- tidyr::unnest(.data, bootstrap_samples) %>%
+  df <- tidyr::unnest(.data, bootstrap_samples) |>
     purrr::set_names("sim_number", "y")
 
   # Return ----
diff --git a/R/combine-multi-single-dist-tbl.R b/R/combine-multi-single-dist-tbl.R
index fa7947e0..76e76795 100644
--- a/R/combine-multi-single-dist-tbl.R
+++ b/R/combine-multi-single-dist-tbl.R
@@ -68,22 +68,22 @@ tidy_multi_single_dist <- function(.tidy_dist = NULL,
   names(param_grid) <- methods::formalArgs(td)
 
   # Run call on the grouped df ----
-  dff <- param_grid %>%
+  dff <- param_grid |>
     dplyr::mutate(results = purrr::pmap(dplyr::pick(dplyr::everything()), match.fun(td)))
     #dplyr::mutate(results = purrr::pmap(dplyr::cur_data(), match.fun(td)))
 
   # Get the attributes to be used later on ----
-  atb <- dff$results[[1]] %>% attributes()
+  atb <- dff$results[[1]] |> attributes()
 
   # Make Dist Type for column ----
-  # dist_type <- stringr::str_remove(atb$tibble_type, "tidy_") %>%
-  #   stringr::str_replace_all(pattern = "_", " ") %>%
+  # dist_type <- stringr::str_remove(atb$tibble_type, "tidy_") |>
+  #   stringr::str_replace_all(pattern = "_", " ") |>
   #   stringr::str_to_title()
 
   dist_type <- dist_type_extractor(atb$tibble_type)
 
   # Get column names from the param_grid in order to make teh dist_type column ----
-  cols <- names(param_grid %>% dplyr::select(-c(.n, .num_sims)))
+  cols <- names(param_grid |> dplyr::select(-c(.n, .num_sims)))
 
   if (length(cols) == 1) {
     dff$dist_name <- paste0(dist_type, " c(", dff[, cols], ")")
@@ -101,11 +101,11 @@ tidy_multi_single_dist <- function(.tidy_dist = NULL,
   #   ")"
   # )
 
-  df_unnested_tbl <- dff %>%
-    tidyr::unnest(results) %>%
-    dplyr::ungroup() %>%
-    dplyr::select(sim_number, dist_name, x:q) %>%
-    dplyr::mutate(dist_name = as.factor(dist_name)) %>%
+  df_unnested_tbl <- dff |>
+    tidyr::unnest(results) |>
+    dplyr::ungroup() |>
+    dplyr::select(sim_number, dist_name, x:q) |>
+    dplyr::mutate(dist_name = as.factor(dist_name)) |>
     dplyr::arrange(sim_number, dist_name)
 
   # Attach attributes ----
diff --git a/R/combine-tidy-distributions-tbl.R b/R/combine-tidy-distributions-tbl.R
index 67cdb17c..49a365aa 100644
--- a/R/combine-tidy-distributions-tbl.R
+++ b/R/combine-tidy-distributions-tbl.R
@@ -57,10 +57,10 @@ tidy_combine_distributions <- function(...) {
   # Get the distribution type
   dist_final_tbl <- purrr::map(
     .x = dist_list,
-    .f = ~ .x %>%
-      dplyr::mutate(dist_type = attributes(.x)[["dist_with_params"]]) %>%
+    .f = ~ .x |>
+      dplyr::mutate(dist_type = attributes(.x)[["dist_with_params"]]) |>
       dplyr::mutate(dist_type = as.factor(dist_type))
-  ) %>%
+  ) |>
     purrr::map_dfr(dplyr::as_tibble)
 
   attr(dist_final_tbl, "tibble_type") <- "tidy_multi_dist_combine"
diff --git a/R/empirical-tidy-bootstrap.R b/R/empirical-tidy-bootstrap.R
index 1403bc1e..5e09f594 100644
--- a/R/empirical-tidy-bootstrap.R
+++ b/R/empirical-tidy-bootstrap.R
@@ -74,11 +74,11 @@ tidy_bootstrap <- function(.x, .num_sims = 2000, .proportion = 0.8,
   }
 
   # Data ----
-  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) %>%
-    dplyr::group_by(sim_number) %>%
+  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) |>
+    dplyr::group_by(sim_number) |>
     dplyr::mutate(bootstrap_samples = list(
       sample(x = x_term, size = floor(prop * n), replace = TRUE)
-    )) %>%
+    )) |>
     dplyr::ungroup()
 
   # Attach descriptive attributes to tibble
diff --git a/R/empirical-tidy.R b/R/empirical-tidy.R
index ee307ea7..0be05104 100644
--- a/R/empirical-tidy.R
+++ b/R/empirical-tidy.R
@@ -41,35 +41,35 @@ tidy_empirical <- function(.x, .num_sims = 1, .distribution_type = "continuous")
   ## New P
   e <- stats::ecdf(x_term)
 
-  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) %>%
-    dplyr::group_by(sim_number) %>%
-    dplyr::mutate(x = list(1:n)) %>%
+  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) |>
+    dplyr::group_by(sim_number) |>
+    dplyr::mutate(x = list(1:n)) |>
     dplyr::mutate(y = ifelse(
       num_sims == 1,
       list(x_term),
       list(sample(x_term, replace = TRUE))
-    )) %>%
-    dplyr::mutate(d = list(density(unlist(y), n = n)[c("x", "y")] %>%
-      purrr::set_names("dx", "dy") %>%
-      dplyr::as_tibble())) %>%
-    dplyr::mutate(p = list(e(unlist(y)))) %>%
-    dplyr::mutate(q = NA) %>%
-    tidyr::unnest(cols = c(x, y, d, p, q)) %>%
+    )) |>
+    dplyr::mutate(d = list(density(unlist(y), n = n)[c("x", "y")] |>
+      purrr::set_names("dx", "dy") |>
+      dplyr::as_tibble())) |>
+    dplyr::mutate(p = list(e(unlist(y)))) |>
+    dplyr::mutate(q = NA) |>
+    tidyr::unnest(cols = c(x, y, d, p, q)) |>
     dplyr::ungroup()
 
-  q_vec <- df %>%
-    dplyr::select(sim_number, y) %>%
-    dplyr::group_by(sim_number) %>%
+  q_vec <- df |>
+    dplyr::select(sim_number, y) |>
+    dplyr::group_by(sim_number) |>
     dplyr::mutate(
       q = rep(
         stats::quantile(y, probs = seq(0, 1, 1 / (n - 1)), type = 1),
         1
       )
-    ) %>%
-    dplyr::ungroup() %>%
+    ) |>
+    dplyr::ungroup() |>
     dplyr::select(q)
 
-  df <- df %>%
+  df <- df |>
     dplyr::mutate(q = q_vec$q)
 
   # Attach descriptive attributes to tibble
@@ -108,24 +108,24 @@ tidy_empirical <- function(.x, .num_sims = 1, .distribution_type = "continuous")
 #
 #   p_vec <- dft$y
 #
-#   df <- dplyr::tibble(sim_number = as.factor(1)) %>%
-#     dplyr::group_by(sim_number) %>%
-#     dplyr::mutate(x = list(1:n)) %>%
-#     dplyr::mutate(y = NA) %>%
-#     dplyr::mutate(d = list(density(unlist(x), n = n)[c("x", "y")] %>%
-#       purrr::set_names("dx", "dy") %>%
-#       dplyr::as_tibble())) %>%
-#     dplyr::mutate(p = list(p_vec)) %>%
-#     dplyr::mutate(q = NA) %>%
-#     dplyr::mutate(y = list(d[[1]][["dy"]])) %>%
-#     tidyr::unnest(cols = c(x, y, d, p, q)) %>%
+#   df <- dplyr::tibble(sim_number = as.factor(1)) |>
+#     dplyr::group_by(sim_number) |>
+#     dplyr::mutate(x = list(1:n)) |>
+#     dplyr::mutate(y = NA) |>
+#     dplyr::mutate(d = list(density(unlist(x), n = n)[c("x", "y")] |>
+#       purrr::set_names("dx", "dy") |>
+#       dplyr::as_tibble())) |>
+#     dplyr::mutate(p = list(p_vec)) |>
+#     dplyr::mutate(q = NA) |>
+#     dplyr::mutate(y = list(d[[1]][["dy"]])) |>
+#     tidyr::unnest(cols = c(x, y, d, p, q)) |>
 #     dplyr::ungroup()
 #
-#   q_vec <- stats::quantile(df$y, probs = seq(0, 1, 1 / (n - 1)), type = 1) %>%
-#     dplyr::as_tibble() %>%
+#   q_vec <- stats::quantile(df$y, probs = seq(0, 1, 1 / (n - 1)), type = 1) |>
+#     dplyr::as_tibble() |>
 #     dplyr::rename("q" = "value")
 #
-#   df <- df %>%
+#   df <- df |>
 #     dplyr::mutate(q = q_vec$q)
 #
 #   attr(df, ".x") <- .x
diff --git a/R/est-param-bernoulli.R b/R/est-param-bernoulli.R
index 84d89a2f..dd00146b 100644
--- a/R/est-param-bernoulli.R
+++ b/R/est-param-bernoulli.R
@@ -25,12 +25,12 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' tb <- tidy_bernoulli(.prob = .1) %>% pull(y)
+#' tb <- tidy_bernoulli(.prob = .1) |> pull(y)
 #' output <- util_bernoulli_param_estimate(tb)
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-beta.R b/R/est-param-beta.R
index 7cebaa96..bfbbd4a2 100644
--- a/R/est-param-beta.R
+++ b/R/est-param-beta.R
@@ -38,7 +38,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' tb <- rbeta(50, 2.5, 1.4)
diff --git a/R/est-param-binomial.R b/R/est-param-binomial.R
index b371ef36..fd412152 100644
--- a/R/est-param-binomial.R
+++ b/R/est-param-binomial.R
@@ -32,7 +32,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-burr.R b/R/est-param-burr.R
index f04a14cd..54d5259e 100644
--- a/R/est-param-burr.R
+++ b/R/est-param-burr.R
@@ -25,12 +25,12 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) %>% pull(y)
+#' tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y)
 #' output <- util_burr_param_estimate(tb)
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-cauchy.R b/R/est-param-cauchy.R
index 1b2b7a12..b06dc23a 100644
--- a/R/est-param-cauchy.R
+++ b/R/est-param-cauchy.R
@@ -28,7 +28,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-exponential.R b/R/est-param-exponential.R
index 22340cd7..06d07d20 100644
--- a/R/est-param-exponential.R
+++ b/R/est-param-exponential.R
@@ -23,12 +23,12 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' te <- tidy_exponential(.rate = .1) %>% pull(y)
+#' te <- tidy_exponential(.rate = .1) |> pull(y)
 #' output <- util_exponential_param_estimate(te)
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-gamma.R b/R/est-param-gamma.R
index e2fa35d3..778b8d7a 100644
--- a/R/est-param-gamma.R
+++ b/R/est-param-gamma.R
@@ -23,12 +23,12 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' tg <- tidy_gamma(.shape = 1, .scale = .3) %>% pull(y)
+#' tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y)
 #' output <- util_gamma_param_estimate(tg)
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-geometric.R b/R/est-param-geometric.R
index 0d315805..3ca12d44 100644
--- a/R/est-param-geometric.R
+++ b/R/est-param-geometric.R
@@ -25,12 +25,12 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' tg <- tidy_geometric(.prob = .1) %>% pull(y)
+#' tg <- tidy_geometric(.prob = .1) |> pull(y)
 #' output <- util_geometric_param_estimate(tg)
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-hypergeometric.R b/R/est-param-hypergeometric.R
index ebce317c..c187138c 100644
--- a/R/est-param-hypergeometric.R
+++ b/R/est-param-hypergeometric.R
@@ -44,7 +44,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/est-param-logistic.R b/R/est-param-logistic.R
index f39700a7..4c356b03 100644
--- a/R/est-param-logistic.R
+++ b/R/est-param-logistic.R
@@ -33,7 +33,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' t <- rlogis(50, 2.5, 1.4)
diff --git a/R/est-param-lognormal.R b/R/est-param-lognormal.R
index 39509f13..125f2675 100644
--- a/R/est-param-lognormal.R
+++ b/R/est-param-lognormal.R
@@ -32,10 +32,10 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
-#' tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) %>% pull(y)
+#' tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y)
 #' util_lognormal_param_estimate(tb)$parameter_tbl
 #'
 #' @return
diff --git a/R/est-param-negative-binomial.R b/R/est-param-negative-binomial.R
index 24b652df..0ec22dea 100644
--- a/R/est-param-negative-binomial.R
+++ b/R/est-param-negative-binomial.R
@@ -33,7 +33,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' t <- rnbinom(50, 1, .1)
diff --git a/R/est-param-normal.R b/R/est-param-normal.R
index 75f5cddb..6182f6da 100644
--- a/R/est-param-normal.R
+++ b/R/est-param-normal.R
@@ -32,7 +32,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' t <- rnorm(50, 0, 1)
diff --git a/R/est-param-pareto.R b/R/est-param-pareto.R
index d7fd31ae..5e51a665 100644
--- a/R/est-param-pareto.R
+++ b/R/est-param-pareto.R
@@ -32,10 +32,10 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
-#' t <- tidy_pareto(50, 1, 1) %>% pull(y)
+#' t <- tidy_pareto(50, 1, 1) |> pull(y)
 #' util_pareto_param_estimate(t)$parameter_tbl
 #'
 #' @return
diff --git a/R/est-param-poisson.R b/R/est-param-poisson.R
index 6c9a7487..320fb2b9 100644
--- a/R/est-param-poisson.R
+++ b/R/est-param-poisson.R
@@ -28,7 +28,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' t <- rpois(50, 5)
diff --git a/R/est-param-uniform.R b/R/est-param-uniform.R
index 4f0aed6e..5902d7d5 100644
--- a/R/est-param-uniform.R
+++ b/R/est-param-uniform.R
@@ -28,7 +28,7 @@
 #'
 #' output$parameter_tbl
 #'
-#' output$combined_data_tbl %>%
+#' output$combined_data_tbl |>
 #'   tidy_combined_autoplot()
 #'
 #' @return
diff --git a/R/palette-color-blind.R b/R/palette-color-blind.R
index d88bbafc..20ba2b3f 100644
--- a/R/palette-color-blind.R
+++ b/R/palette-color-blind.R
@@ -19,7 +19,7 @@ color_blind <- function() {
 #' @export
 td_scale_fill_colorblind <- function(..., theme = "td") {
   pal <- switch(theme,
-    "td" = unname(color_blind()) %>% rep(100)
+    "td" = unname(color_blind()) |> rep(100)
   )
 
   ggplot2::scale_fill_manual(values = pal)
@@ -33,7 +33,7 @@ td_scale_fill_colorblind <- function(..., theme = "td") {
 #' @export
 td_scale_color_colorblind <- function(..., theme = "td") {
   pal <- switch(theme,
-    "td" = unname(color_blind()) %>% rep(100)
+    "td" = unname(color_blind()) |> rep(100)
   )
 
   ggplot2::scale_color_manual(values = pal)
diff --git a/R/plot-bootstrap-stat.R b/R/plot-bootstrap-stat.R
index 46729c00..2804a01c 100644
--- a/R/plot-bootstrap-stat.R
+++ b/R/plot-bootstrap-stat.R
@@ -42,10 +42,10 @@
 #' @examples
 #' x <- mtcars$mpg
 #'
-#' tidy_bootstrap(x) %>%
+#' tidy_bootstrap(x) |>
 #'   bootstrap_stat_plot(y, "cmean")
 #'
-#' tidy_bootstrap(x, .num_sims = 10) %>%
+#' tidy_bootstrap(x, .num_sims = 10) |>
 #'   bootstrap_stat_plot(y,
 #'     .stat = "chmean", .show_groups = TRUE,
 #'     .show_ci_label = FALSE
@@ -99,7 +99,7 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
 
   # Data ----
   if (atb$tibble_type == "tidy_bootstrap_nested") {
-    df_tbl <- dplyr::as_tibble(.data) %>%
+    df_tbl <- dplyr::as_tibble(.data) |>
       TidyDensity::bootstrap_unnest_tbl()
   }
 
@@ -109,8 +109,8 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
 
   # Manipulation
   if (show_groups) {
-    df_tbl <- df_tbl %>%
-      dplyr::group_by(sim_number) %>%
+    df_tbl <- df_tbl |>
+      dplyr::group_by(sim_number) |>
       dplyr::mutate(
         stat = switch(stat_fn,
           "cmean" = TidyDensity::cmean(y),
@@ -126,17 +126,17 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
           "cskewness" = TidyDensity::cskewness(y),
           "ckurtosis" = TidyDensity::ckurtosis(y)
         )
-      ) %>%
-      dplyr::mutate(x = dplyr::row_number()) %>%
-      dplyr::ungroup() %>%
-      dplyr::group_by(x) %>%
-      dplyr::mutate(cil = TidyDensity::ci_lo(stat, .na_rm = TRUE)) %>%
-      dplyr::mutate(cih = TidyDensity::ci_hi(stat, .na_rm = TRUE)) %>%
-      dplyr::mutate(mstat = mean(stat, na.rm = TRUE)) %>%
+      ) |>
+      dplyr::mutate(x = dplyr::row_number()) |>
+      dplyr::ungroup() |>
+      dplyr::group_by(x) |>
+      dplyr::mutate(cil = TidyDensity::ci_lo(stat, .na_rm = TRUE)) |>
+      dplyr::mutate(cih = TidyDensity::ci_hi(stat, .na_rm = TRUE)) |>
+      dplyr::mutate(mstat = mean(stat, na.rm = TRUE)) |>
       dplyr::ungroup()
   } else {
-    df_tbl <- df_tbl %>%
-      dplyr::group_by(sim_number) %>%
+    df_tbl <- df_tbl |>
+      dplyr::group_by(sim_number) |>
       dplyr::mutate(
         stat = switch(stat_fn,
           "cmean" = TidyDensity::cmean(y),
@@ -152,15 +152,15 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
           "cskewness" = TidyDensity::cskewness(y),
           "ckurtosis" = TidyDensity::ckurtosis(y)
         )
-      ) %>%
-      dplyr::mutate(x = dplyr::row_number()) %>%
-      dplyr::ungroup() %>%
-      dplyr::group_by(x) %>%
+      ) |>
+      dplyr::mutate(x = dplyr::row_number()) |>
+      dplyr::ungroup() |>
+      dplyr::group_by(x) |>
       dplyr::summarise(
         cil = TidyDensity::ci_lo(stat, .na_rm = TRUE),
         cih = TidyDensity::ci_hi(stat, .na_rm = TRUE),
         stat = mean(stat, na.rm = TRUE)
-      ) %>%
+      ) |>
       dplyr::ungroup()
   }
 
@@ -183,7 +183,7 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
   sub_title <- paste0("Cumulative Statistic: ", y_txt)
   cap <- paste0("Simulations: ", atb$.num_sims)
   if (show_groups) {
-    p <- df_tbl %>%
+    p <- df_tbl |>
       ggplot2::ggplot(ggplot2::aes(x = x, y = stat, group = sim_number)) +
       ggplot2::geom_line(color = "lightgray") +
       ggplot2::geom_line(ggplot2::aes(y = mstat),
@@ -206,7 +206,7 @@ bootstrap_stat_plot <- function(.data, .value, .stat = "cmean",
         caption = cap
       )
   } else {
-    p <- df_tbl %>%
+    p <- df_tbl |>
       ggplot2::ggplot(ggplot2::aes(x = x, y = stat)) +
       ggplot2::geom_line(color = "red", linetype = "dashed") +
       ggplot2::geom_line(ggplot2::aes(y = cil),
diff --git a/R/stats-bernoulli-tbl.R b/R/stats-bernoulli-tbl.R
index 56329c72..dab3b78f 100644
--- a/R/stats-bernoulli-tbl.R
+++ b/R/stats-bernoulli-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_bernoulli() %>%
-#'   util_bernoulli_stats_tbl() %>%
+#' tidy_bernoulli() |>
+#'   util_bernoulli_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-beta-tbl.R b/R/stats-beta-tbl.R
index b72640eb..6ac55163 100644
--- a/R/stats-beta-tbl.R
+++ b/R/stats-beta-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_beta() %>%
-#'   util_beta_stats_tbl() %>%
+#' tidy_beta() |>
+#'   util_beta_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-binomial-tbl.R b/R/stats-binomial-tbl.R
index 945ff5a0..962003cf 100644
--- a/R/stats-binomial-tbl.R
+++ b/R/stats-binomial-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_binomial() %>%
-#'   util_binomial_stats_tbl() %>%
+#' tidy_binomial() |>
+#'   util_binomial_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-burr-tbl.R b/R/stats-burr-tbl.R
index f2268ee8..dac88618 100644
--- a/R/stats-burr-tbl.R
+++ b/R/stats-burr-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_burr() %>%
-#'   util_burr_stats_tbl() %>%
+#' tidy_burr() |>
+#'   util_burr_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-cauchy-tbl.R b/R/stats-cauchy-tbl.R
index f72cff43..07ecae02 100644
--- a/R/stats-cauchy-tbl.R
+++ b/R/stats-cauchy-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_cauchy() %>%
-#'   util_cauchy_stats_tbl() %>%
+#' tidy_cauchy() |>
+#'   util_cauchy_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-chisquare-tbl.R b/R/stats-chisquare-tbl.R
index 91ee161b..c0c541b8 100644
--- a/R/stats-chisquare-tbl.R
+++ b/R/stats-chisquare-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_chisquare() %>%
-#'   util_chisquare_stats_tbl() %>%
+#' tidy_chisquare() |>
+#'   util_chisquare_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-exponential-tbl.R b/R/stats-exponential-tbl.R
index a6982b59..4bcaa4ad 100644
--- a/R/stats-exponential-tbl.R
+++ b/R/stats-exponential-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_exponential() %>%
-#'   util_exponential_stats_tbl() %>%
+#' tidy_exponential() |>
+#'   util_exponential_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-f-tbl.R b/R/stats-f-tbl.R
index 4006b914..d8ec01a4 100644
--- a/R/stats-f-tbl.R
+++ b/R/stats-f-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_f() %>%
-#'   util_f_stats_tbl() %>%
+#' tidy_f() |>
+#'   util_f_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-gamma-tbl.R b/R/stats-gamma-tbl.R
index 588cef36..8812ae2a 100644
--- a/R/stats-gamma-tbl.R
+++ b/R/stats-gamma-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_gamma() %>%
-#'   util_gamma_stats_tbl() %>%
+#' tidy_gamma() |>
+#'   util_gamma_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-geometric-tbl.R b/R/stats-geometric-tbl.R
index 90c90f23..537f8025 100644
--- a/R/stats-geometric-tbl.R
+++ b/R/stats-geometric-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_geometric() %>%
-#'   util_geometric_stats_tbl() %>%
+#' tidy_geometric() |>
+#'   util_geometric_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
@@ -50,9 +50,9 @@ util_geometric_stats_tbl <- function(.data) {
   p <- atb$.prob
 
   stat_mean <- (1 - p) / p
-  stat_mode <- data_tbl %>%
-    dplyr::filter(p == max(p)) %>%
-    dplyr::pull(y) %>%
+  stat_mode <- data_tbl |>
+    dplyr::filter(p == max(p)) |>
+    dplyr::pull(y) |>
     max()
   stat_sd <- sqrt((1 - p) / p)
   stat_skewness <- (2 - p) / sqrt(1 - p)
diff --git a/R/stats-hypergeometric-tbl.R b/R/stats-hypergeometric-tbl.R
index cdb52e95..e2ac8e68 100644
--- a/R/stats-hypergeometric-tbl.R
+++ b/R/stats-hypergeometric-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_hypergeometric() %>%
-#'   util_hypergeometric_stats_tbl() %>%
+#' tidy_hypergeometric() |>
+#'   util_hypergeometric_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-logistic-tbl.R b/R/stats-logistic-tbl.R
index 6c5c8822..9a71b12d 100644
--- a/R/stats-logistic-tbl.R
+++ b/R/stats-logistic-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_logistic() %>%
-#'   util_logistic_stats_tbl() %>%
+#' tidy_logistic() |>
+#'   util_logistic_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-lognormal-tbl.R b/R/stats-lognormal-tbl.R
index 491e7758..517f9ded 100644
--- a/R/stats-lognormal-tbl.R
+++ b/R/stats-lognormal-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_lognormal() %>%
-#'   util_lognormal_stats_tbl() %>%
+#' tidy_lognormal() |>
+#'   util_lognormal_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-negative-binomial-tbl.R b/R/stats-negative-binomial-tbl.R
index d1afe521..6fa7644a 100644
--- a/R/stats-negative-binomial-tbl.R
+++ b/R/stats-negative-binomial-tbl.R
@@ -17,8 +17,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_negative_binomial() %>%
-#'   util_negative_binomial_stats_tbl() %>%
+#' tidy_negative_binomial() |>
+#'   util_negative_binomial_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-normal-tbl.R b/R/stats-normal-tbl.R
index c5b91438..fc2e1f21 100644
--- a/R/stats-normal-tbl.R
+++ b/R/stats-normal-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_normal() %>%
-#'   util_normal_stats_tbl() %>%
+#' tidy_normal() |>
+#'   util_normal_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-pareto-tbl.R b/R/stats-pareto-tbl.R
index fc2c3806..7e72268e 100644
--- a/R/stats-pareto-tbl.R
+++ b/R/stats-pareto-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_pareto() %>%
-#'   util_pareto_stats_tbl() %>%
+#' tidy_pareto() |>
+#'   util_pareto_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-poisson-tbl.R b/R/stats-poisson-tbl.R
index cf23a0ca..b39379ba 100644
--- a/R/stats-poisson-tbl.R
+++ b/R/stats-poisson-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_poisson() %>%
-#'   util_poisson_stats_tbl() %>%
+#' tidy_poisson() |>
+#'   util_poisson_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-t-tbl.R b/R/stats-t-tbl.R
index 1b4d1b89..cd69121c 100644
--- a/R/stats-t-tbl.R
+++ b/R/stats-t-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_t() %>%
-#'   util_t_stats_tbl() %>%
+#' tidy_t() |>
+#'   util_t_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-tidy-stat-func-tbl.R b/R/stats-tidy-stat-func-tbl.R
index 91cd84a7..6fa07df3 100644
--- a/R/stats-tidy-stat-func-tbl.R
+++ b/R/stats-tidy-stat-func-tbl.R
@@ -107,13 +107,13 @@ tidy_stat_tbl <- function(.data, .x = y, .fns, .return_type = "vector",
     # # Benchmark ran 25 at 15.13 seconds
     # # Thank you Akrun https://stackoverflow.com/questions/73938515/keep-names-from-quantile-function-when-used-in-a-data-table/73938561#73938561
     if (atb$tibble_type == "tidy_bootstrap_nested") {
-        dt <- dplyr::as_tibble(.data) %>%
-            TidyDensity::bootstrap_unnest_tbl() %>%
-            dplyr::select(sim_number, {{ value_var_expr }}) %>%
+        dt <- dplyr::as_tibble(.data) |>
+            TidyDensity::bootstrap_unnest_tbl() |>
+            dplyr::select(sim_number, {{ value_var_expr }}) |>
             data.table::as.data.table()
     } else {
-        dt <- dplyr::as_tibble(.data) %>%
-            dplyr::select(sim_number, {{ value_var_expr }}) %>%
+        dt <- dplyr::as_tibble(.data) |>
+            dplyr::select(sim_number, {{ value_var_expr }}) |>
             data.table::as.data.table()
     }
 
@@ -123,9 +123,9 @@ tidy_stat_tbl <- function(.data, .x = y, .fns, .return_type = "vector",
       dt[, as.list(func(.SD[[1]], ...)), by = sim_number, .SDcols = .x],
       id.var = "sim_number",
       value.name = func_chr
-    ) %>%
-      dplyr::as_tibble() %>%
-      dplyr::arrange(sim_number, variable) %>%
+    ) |>
+      dplyr::as_tibble() |>
+      dplyr::arrange(sim_number, variable) |>
       dplyr::rename(name = variable)
 
     return(ret)
@@ -134,24 +134,24 @@ tidy_stat_tbl <- function(.data, .x = y, .fns, .return_type = "vector",
   # Check to see if it is a bootstrap tibble first
   # Is it a Bootstrap Nested tibble?
   if (atb$tibble_type == "tidy_bootstrap_nested") {
-    df_tbl <- dplyr::as_tibble(.data) %>%
-      TidyDensity::bootstrap_unnest_tbl() %>%
-      split(.$sim_number) %>%
-      purrr::map(.f = ~ .x %>% dplyr::pull(y))
+    df_tbl <- dplyr::as_tibble(.data) |>
+      TidyDensity::bootstrap_unnest_tbl()
+    df_tbl <- base::split(df_tbl, df_tbl$sim_number) |>
+      purrr::map(\(x) x |> dplyr::pull(y))
   }
 
   # Is it an unnested bootstrap tibble?
   if (atb$tibble_type == "tidy_bootstrap") {
-    df_tbl <- dplyr::as_tibble(.data) %>%
-      split(.$sim_number) %>%
-      purrr::map(.f = ~ .x %>% dplyr::pull(y))
+    df_tbl <- dplyr::as_tibble(.data)
+    df_tbl <- base::split(df_tbl, df_tbl$sim_number) |>
+      purrr::map(\(x) x |> dplyr::pull(y))
   }
 
   # If regular tidy_ dist tibble ----
   if (!atb$tibble_type %in% c("tidy_bootstrap", "tidy_bootstrap_nested")) {
-    df_tbl <- dplyr::as_tibble(.data) %>%
-      split(.$sim_number) %>%
-      purrr::map(.f = ~ .x %>% dplyr::pull({{ value_var_expr }}))
+    df_tbl <- dplyr::as_tibble(.data)
+    df_tbl <- base::split(df_tbl, df_tbl$sim_number) |>
+      purrr::map(\(x) x |> dplyr::pull({{ value_var_expr }}))
   }
 
   # New Param Args ----
@@ -205,22 +205,22 @@ tidy_stat_tbl <- function(.data, .x = y, .fns, .return_type = "vector",
     )
 
     if (is.null(args)) {
-      ret <- ret %>%
-        purrr::map(~ cbind(.x, name = names(.x))) %>%
-        purrr::imap(~ cbind(.x, sim_number = .y)) %>%
-        purrr::map_df(dplyr::as_tibble) %>%
-        dplyr::select(sim_number, .x, dplyr::everything()) %>%
-        dplyr::mutate(.x = as.numeric(.x)) %>%
-        dplyr::mutate(sim_number = factor(sim_number)) %>%
+      ret <- ret |>
+        purrr::map(~ cbind(.x, name = names(.x))) |>
+        purrr::imap(~ cbind(.x, sim_number = .y)) |>
+        purrr::map_df(dplyr::as_tibble) |>
+        dplyr::select(sim_number, .x, dplyr::everything()) |>
+        dplyr::mutate(.x = as.numeric(.x)) |>
+        dplyr::mutate(sim_number = factor(sim_number)) |>
         dplyr::rename(value = .x)
     } else {
-      ret <- ret %>%
-        purrr::map(~ cbind(.x, name = names(.x))) %>%
-        purrr::imap(.f = ~ cbind(.x, sim_number = .y)) %>%
-        purrr::map_df(dplyr::as_tibble) %>%
-        dplyr::select(sim_number, .x, dplyr::everything()) %>%
-        dplyr::mutate(.x = as.numeric(.x)) %>%
-        dplyr::mutate(sim_number = factor(sim_number)) %>%
+      ret <- ret |>
+        purrr::map(~ cbind(.x, name = names(.x))) |>
+        purrr::imap(.f = ~ cbind(.x, sim_number = .y)) |>
+        purrr::map_df(dplyr::as_tibble) |>
+        dplyr::select(sim_number, .x, dplyr::everything()) |>
+        dplyr::mutate(.x = as.numeric(.x)) |>
+        dplyr::mutate(sim_number = factor(sim_number)) |>
         dplyr::rename(value = .x)
     }
 
@@ -228,13 +228,13 @@ tidy_stat_tbl <- function(.data, .x = y, .fns, .return_type = "vector",
     if ("name" %in% names(ret)) {
       names(ret) <- cn
     } else {
-      ret <- ret %>%
+      ret <- ret |>
         dplyr::mutate(name = 1)
 
       names(ret) <- cn
     }
 
-    ret <- ret %>% dplyr::select(sim_number, name, dplyr::everything())
+    ret <- ret |> dplyr::select(sim_number, name, dplyr::everything())
   }
 
   # Return
diff --git a/R/stats-uniform-tbl.R b/R/stats-uniform-tbl.R
index 9bd2c259..c142c664 100644
--- a/R/stats-uniform-tbl.R
+++ b/R/stats-uniform-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_uniform() %>%
-#'   util_uniform_stats_tbl() %>%
+#' tidy_uniform() |>
+#'   util_uniform_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/R/stats-weibull-tbl.R b/R/stats-weibull-tbl.R
index 8be7e19c..025e73f2 100644
--- a/R/stats-weibull-tbl.R
+++ b/R/stats-weibull-tbl.R
@@ -16,8 +16,8 @@
 #' @examples
 #' library(dplyr)
 #'
-#' tidy_weibull() %>%
-#'   util_weibull_stats_tbl() %>%
+#' tidy_weibull() |>
+#'   util_weibull_stats_tbl() |>
 #'   glimpse()
 #'
 #' @return
diff --git a/man/bootstrap_density_augment.Rd b/man/bootstrap_density_augment.Rd
index e7c5b62d..33b270de 100644
--- a/man/bootstrap_density_augment.Rd
+++ b/man/bootstrap_density_augment.Rd
@@ -29,11 +29,11 @@ those functions.
 \examples{
 x <- mtcars$mpg
 
-tidy_bootstrap(x) \%>\%
+tidy_bootstrap(x) |>
   bootstrap_density_augment()
 
-tidy_bootstrap(x) \%>\%
-  bootstrap_unnest_tbl() \%>\%
+tidy_bootstrap(x) |>
+  bootstrap_unnest_tbl() |>
   bootstrap_density_augment()
 
 }
diff --git a/man/bootstrap_p_augment.Rd b/man/bootstrap_p_augment.Rd
index 5beab799..b9109d1a 100644
--- a/man/bootstrap_p_augment.Rd
+++ b/man/bootstrap_p_augment.Rd
@@ -27,8 +27,8 @@ tibble.
 }
 \examples{
 x <- mtcars$mpg
-tidy_bootstrap(x) \%>\%
-  bootstrap_unnest_tbl() \%>\%
+tidy_bootstrap(x) |>
+  bootstrap_unnest_tbl() |>
   bootstrap_p_augment(y)
 
 }
diff --git a/man/bootstrap_q_augment.Rd b/man/bootstrap_q_augment.Rd
index 4cffe071..5db82dcf 100644
--- a/man/bootstrap_q_augment.Rd
+++ b/man/bootstrap_q_augment.Rd
@@ -28,8 +28,8 @@ tibble.
 \examples{
 x <- mtcars$mpg
 
-tidy_bootstrap(x) \%>\%
-  bootstrap_unnest_tbl() \%>\%
+tidy_bootstrap(x) |>
+  bootstrap_unnest_tbl() |>
   bootstrap_q_augment(y)
 
 }
diff --git a/man/bootstrap_stat_plot.Rd b/man/bootstrap_stat_plot.Rd
index c7224be5..2b300d6f 100644
--- a/man/bootstrap_stat_plot.Rd
+++ b/man/bootstrap_stat_plot.Rd
@@ -61,10 +61,10 @@ cumulative functions that can be applied to the data.The accepted values are:
 \examples{
 x <- mtcars$mpg
 
-tidy_bootstrap(x) \%>\%
+tidy_bootstrap(x) |>
   bootstrap_stat_plot(y, "cmean")
 
-tidy_bootstrap(x, .num_sims = 10) \%>\%
+tidy_bootstrap(x, .num_sims = 10) |>
   bootstrap_stat_plot(y,
     .stat = "chmean", .show_groups = TRUE,
     .show_ci_label = FALSE
diff --git a/man/bootstrap_unnest_tbl.Rd b/man/bootstrap_unnest_tbl.Rd
index ae74046e..70b64483 100644
--- a/man/bootstrap_unnest_tbl.Rd
+++ b/man/bootstrap_unnest_tbl.Rd
@@ -26,7 +26,7 @@ not work unless the data comes from that function.
 tb <- tidy_bootstrap(.x = mtcars$mpg)
 bootstrap_unnest_tbl(tb)
 
-bootstrap_unnest_tbl(tb) \%>\%
+bootstrap_unnest_tbl(tb) |>
   tidy_distribution_summary_tbl(sim_number)
 
 }
diff --git a/man/tidy_combined_autoplot.Rd b/man/tidy_combined_autoplot.Rd
index 72f3cd2c..351df3d7 100644
--- a/man/tidy_combined_autoplot.Rd
+++ b/man/tidy_combined_autoplot.Rd
@@ -77,10 +77,10 @@ combined_tbl <- tidy_combine_distributions(
 
 combined_tbl
 
-combined_tbl \%>\%
+combined_tbl |>
   tidy_combined_autoplot()
 
-combined_tbl \%>\%
+combined_tbl |>
   tidy_combined_autoplot(.plot_type = "qq")
 
 }
diff --git a/man/tidy_four_autoplot.Rd b/man/tidy_four_autoplot.Rd
index 9da351c7..409edd59 100644
--- a/man/tidy_four_autoplot.Rd
+++ b/man/tidy_four_autoplot.Rd
@@ -67,7 +67,7 @@ This function will spit out one of the following plots:
 }
 }
 \examples{
-tidy_normal(.num_sims = 5) \%>\%
+tidy_normal(.num_sims = 5) |>
   tidy_four_autoplot()
 
 }
diff --git a/man/tidy_multi_dist_autoplot.Rd b/man/tidy_multi_dist_autoplot.Rd
index 4a0b0d74..d896eabc 100644
--- a/man/tidy_multi_dist_autoplot.Rd
+++ b/man/tidy_multi_dist_autoplot.Rd
@@ -80,10 +80,10 @@ tn <- tidy_multi_single_dist(
   )
 )
 
-tn \%>\%
+tn |>
   tidy_multi_dist_autoplot()
 
-tn \%>\%
+tn |>
   tidy_multi_dist_autoplot(.plot_type = "qq")
 
 }
diff --git a/man/tidy_random_walk_autoplot.Rd b/man/tidy_random_walk_autoplot.Rd
index e7754f05..a857093d 100644
--- a/man/tidy_random_walk_autoplot.Rd
+++ b/man/tidy_random_walk_autoplot.Rd
@@ -45,12 +45,12 @@ This function will produce a simple random walk plot from a \code{tidy_}
 distribution function.
 }
 \examples{
-tidy_normal(.sd = .1, .num_sims = 5) \%>\%
-  tidy_random_walk(.value_type = "cum_sum") \%>\%
+tidy_normal(.sd = .1, .num_sims = 5) |>
+  tidy_random_walk(.value_type = "cum_sum") |>
   tidy_random_walk_autoplot()
 
-tidy_normal(.sd = .1, .num_sims = 20) \%>\%
-  tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) \%>\%
+tidy_normal(.sd = .1, .num_sims = 20) |>
+  tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |>
   tidy_random_walk_autoplot()
 
 }
diff --git a/man/util_bernoulli_param_estimate.Rd b/man/util_bernoulli_param_estimate.Rd
index d7742ade..403ac337 100644
--- a/man/util_bernoulli_param_estimate.Rd
+++ b/man/util_bernoulli_param_estimate.Rd
@@ -33,12 +33,12 @@ It will attempt to estimate the prob parameter of a Bernoulli distribution.
 library(dplyr)
 library(ggplot2)
 
-tb <- tidy_bernoulli(.prob = .1) \%>\% pull(y)
+tb <- tidy_bernoulli(.prob = .1) |> pull(y)
 output <- util_bernoulli_param_estimate(tb)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_bernoulli_stats_tbl.Rd b/man/util_bernoulli_stats_tbl.Rd
index 19f50c9d..d2bc02ca 100644
--- a/man/util_bernoulli_stats_tbl.Rd
+++ b/man/util_bernoulli_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_bernoulli() \%>\%
-  util_bernoulli_stats_tbl() \%>\%
+tidy_bernoulli() |>
+  util_bernoulli_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_beta_param_estimate.Rd b/man/util_beta_param_estimate.Rd
index 28e314d1..b525ca5d 100644
--- a/man/util_beta_param_estimate.Rd
+++ b/man/util_beta_param_estimate.Rd
@@ -48,7 +48,7 @@ output <- util_beta_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 tb <- rbeta(50, 2.5, 1.4)
diff --git a/man/util_beta_stats_tbl.Rd b/man/util_beta_stats_tbl.Rd
index 4f18c8f6..c07bf032 100644
--- a/man/util_beta_stats_tbl.Rd
+++ b/man/util_beta_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_beta() \%>\%
-  util_beta_stats_tbl() \%>\%
+tidy_beta() |>
+  util_beta_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_binomial_param_estimate.Rd b/man/util_binomial_param_estimate.Rd
index c64f111a..61975ad0 100644
--- a/man/util_binomial_param_estimate.Rd
+++ b/man/util_binomial_param_estimate.Rd
@@ -41,7 +41,7 @@ output <- util_binomial_param_estimate(tb)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_binomial_stats_tbl.Rd b/man/util_binomial_stats_tbl.Rd
index de1b7397..d85b78e8 100644
--- a/man/util_binomial_stats_tbl.Rd
+++ b/man/util_binomial_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_binomial() \%>\%
-  util_binomial_stats_tbl() \%>\%
+tidy_binomial() |>
+  util_binomial_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_burr_param_estimate.Rd b/man/util_burr_param_estimate.Rd
index 1c53fa1b..4517a5af 100644
--- a/man/util_burr_param_estimate.Rd
+++ b/man/util_burr_param_estimate.Rd
@@ -33,12 +33,12 @@ It will attempt to estimate the prob parameter of a Burr distribution.
 library(dplyr)
 library(ggplot2)
 
-tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) \%>\% pull(y)
+tb <- tidy_burr(.shape1 = 1, .shape2 = 2, .rate = .3) |> pull(y)
 output <- util_burr_param_estimate(tb)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_burr_stats_tbl.Rd b/man/util_burr_stats_tbl.Rd
index c488cc65..84a424d2 100644
--- a/man/util_burr_stats_tbl.Rd
+++ b/man/util_burr_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_burr() \%>\%
-  util_burr_stats_tbl() \%>\%
+tidy_burr() |>
+  util_burr_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_cauchy_param_estimate.Rd b/man/util_cauchy_param_estimate.Rd
index 4ada3012..6718707f 100644
--- a/man/util_cauchy_param_estimate.Rd
+++ b/man/util_cauchy_param_estimate.Rd
@@ -36,7 +36,7 @@ output <- util_cauchy_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_cauchy_stats_tbl.Rd b/man/util_cauchy_stats_tbl.Rd
index 5c6bffb8..3e50bbb7 100644
--- a/man/util_cauchy_stats_tbl.Rd
+++ b/man/util_cauchy_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_cauchy() \%>\%
-  util_cauchy_stats_tbl() \%>\%
+tidy_cauchy() |>
+  util_cauchy_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_chisquare_stats_tbl.Rd b/man/util_chisquare_stats_tbl.Rd
index 110cb9a4..4fa5c460 100644
--- a/man/util_chisquare_stats_tbl.Rd
+++ b/man/util_chisquare_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_chisquare() \%>\%
-  util_chisquare_stats_tbl() \%>\%
+tidy_chisquare() |>
+  util_chisquare_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_exponential_param_estimate.Rd b/man/util_exponential_param_estimate.Rd
index b2429472..f2782947 100644
--- a/man/util_exponential_param_estimate.Rd
+++ b/man/util_exponential_param_estimate.Rd
@@ -31,12 +31,12 @@ This function will see if the given vector \code{.x} is a numeric vector.
 library(dplyr)
 library(ggplot2)
 
-te <- tidy_exponential(.rate = .1) \%>\% pull(y)
+te <- tidy_exponential(.rate = .1) |> pull(y)
 output <- util_exponential_param_estimate(te)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_exponential_stats_tbl.Rd b/man/util_exponential_stats_tbl.Rd
index 0a14cacd..b273e2ad 100644
--- a/man/util_exponential_stats_tbl.Rd
+++ b/man/util_exponential_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_exponential() \%>\%
-  util_exponential_stats_tbl() \%>\%
+tidy_exponential() |>
+  util_exponential_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_f_stats_tbl.Rd b/man/util_f_stats_tbl.Rd
index 068e4d44..38b949be 100644
--- a/man/util_f_stats_tbl.Rd
+++ b/man/util_f_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_f() \%>\%
-  util_f_stats_tbl() \%>\%
+tidy_f() |>
+  util_f_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_gamma_param_estimate.Rd b/man/util_gamma_param_estimate.Rd
index a7de4780..93fd5a32 100644
--- a/man/util_gamma_param_estimate.Rd
+++ b/man/util_gamma_param_estimate.Rd
@@ -31,12 +31,12 @@ This function will see if the given vector \code{.x} is a numeric vector.
 library(dplyr)
 library(ggplot2)
 
-tg <- tidy_gamma(.shape = 1, .scale = .3) \%>\% pull(y)
+tg <- tidy_gamma(.shape = 1, .scale = .3) |> pull(y)
 output <- util_gamma_param_estimate(tg)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_gamma_stats_tbl.Rd b/man/util_gamma_stats_tbl.Rd
index 7547239d..b76c9cd7 100644
--- a/man/util_gamma_stats_tbl.Rd
+++ b/man/util_gamma_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_gamma() \%>\%
-  util_gamma_stats_tbl() \%>\%
+tidy_gamma() |>
+  util_gamma_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_geometric_param_estimate.Rd b/man/util_geometric_param_estimate.Rd
index 14848b51..a0110b89 100644
--- a/man/util_geometric_param_estimate.Rd
+++ b/man/util_geometric_param_estimate.Rd
@@ -33,12 +33,12 @@ It will attempt to estimate the prob parameter of a geometric distribution.
 library(dplyr)
 library(ggplot2)
 
-tg <- tidy_geometric(.prob = .1) \%>\% pull(y)
+tg <- tidy_geometric(.prob = .1) |> pull(y)
 output <- util_geometric_param_estimate(tg)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_geometric_stats_tbl.Rd b/man/util_geometric_stats_tbl.Rd
index 359416b0..8da7644f 100644
--- a/man/util_geometric_stats_tbl.Rd
+++ b/man/util_geometric_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_geometric() \%>\%
-  util_geometric_stats_tbl() \%>\%
+tidy_geometric() |>
+  util_geometric_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_hypergeometric_param_estimate.Rd b/man/util_hypergeometric_param_estimate.Rd
index 3fcca644..db2eba52 100644
--- a/man/util_hypergeometric_param_estimate.Rd
+++ b/man/util_hypergeometric_param_estimate.Rd
@@ -61,7 +61,7 @@ output <- util_hypergeometric_param_estimate(th, .total = 50, .k = 5)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_hypergeometric_stats_tbl.Rd b/man/util_hypergeometric_stats_tbl.Rd
index 2e35f055..0575d6b4 100644
--- a/man/util_hypergeometric_stats_tbl.Rd
+++ b/man/util_hypergeometric_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_hypergeometric() \%>\%
-  util_hypergeometric_stats_tbl() \%>\%
+tidy_hypergeometric() |>
+  util_hypergeometric_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_logistic_param_estimate.Rd b/man/util_logistic_param_estimate.Rd
index 926b922b..07ec4651 100644
--- a/man/util_logistic_param_estimate.Rd
+++ b/man/util_logistic_param_estimate.Rd
@@ -43,7 +43,7 @@ output <- util_logistic_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 t <- rlogis(50, 2.5, 1.4)
diff --git a/man/util_logistic_stats_tbl.Rd b/man/util_logistic_stats_tbl.Rd
index 6266ab12..af13f4e2 100644
--- a/man/util_logistic_stats_tbl.Rd
+++ b/man/util_logistic_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_logistic() \%>\%
-  util_logistic_stats_tbl() \%>\%
+tidy_logistic() |>
+  util_logistic_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_lognormal_param_estimate.Rd b/man/util_lognormal_param_estimate.Rd
index 99f27e5f..fda9cb42 100644
--- a/man/util_lognormal_param_estimate.Rd
+++ b/man/util_lognormal_param_estimate.Rd
@@ -42,10 +42,10 @@ output <- util_lognormal_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
-tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) \%>\% pull(y)
+tb <- tidy_lognormal(.meanlog = 2, .sdlog = 1) |> pull(y)
 util_lognormal_param_estimate(tb)$parameter_tbl
 
 }
diff --git a/man/util_lognormal_stats_tbl.Rd b/man/util_lognormal_stats_tbl.Rd
index d28ac994..fcb2904a 100644
--- a/man/util_lognormal_stats_tbl.Rd
+++ b/man/util_lognormal_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_lognormal() \%>\%
-  util_lognormal_stats_tbl() \%>\%
+tidy_lognormal() |>
+  util_lognormal_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_negative_binomial_param_estimate.Rd b/man/util_negative_binomial_param_estimate.Rd
index b35d2c69..652cd139 100644
--- a/man/util_negative_binomial_param_estimate.Rd
+++ b/man/util_negative_binomial_param_estimate.Rd
@@ -44,7 +44,7 @@ output <- util_negative_binomial_param_estimate(x, .size = 1)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 t <- rnbinom(50, 1, .1)
diff --git a/man/util_negative_binomial_stats_tbl.Rd b/man/util_negative_binomial_stats_tbl.Rd
index b792ad8f..36dd0438 100644
--- a/man/util_negative_binomial_stats_tbl.Rd
+++ b/man/util_negative_binomial_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_negative_binomial() \%>\%
-  util_negative_binomial_stats_tbl() \%>\%
+tidy_negative_binomial() |>
+  util_negative_binomial_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_normal_param_estimate.Rd b/man/util_normal_param_estimate.Rd
index 9ff461de..9c8ea52c 100644
--- a/man/util_normal_param_estimate.Rd
+++ b/man/util_normal_param_estimate.Rd
@@ -42,7 +42,7 @@ output <- util_normal_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 t <- rnorm(50, 0, 1)
diff --git a/man/util_normal_stats_tbl.Rd b/man/util_normal_stats_tbl.Rd
index 2895e2e5..af87fcb1 100644
--- a/man/util_normal_stats_tbl.Rd
+++ b/man/util_normal_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_normal() \%>\%
-  util_normal_stats_tbl() \%>\%
+tidy_normal() |>
+  util_normal_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_pareto_param_estimate.Rd b/man/util_pareto_param_estimate.Rd
index 221ddc34..9d5a77f4 100644
--- a/man/util_pareto_param_estimate.Rd
+++ b/man/util_pareto_param_estimate.Rd
@@ -42,10 +42,10 @@ output <- util_pareto_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
-t <- tidy_pareto(50, 1, 1) \%>\% pull(y)
+t <- tidy_pareto(50, 1, 1) |> pull(y)
 util_pareto_param_estimate(t)$parameter_tbl
 
 }
diff --git a/man/util_pareto_stats_tbl.Rd b/man/util_pareto_stats_tbl.Rd
index 997aa7fe..390e0a68 100644
--- a/man/util_pareto_stats_tbl.Rd
+++ b/man/util_pareto_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_pareto() \%>\%
-  util_pareto_stats_tbl() \%>\%
+tidy_pareto() |>
+  util_pareto_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_poisson_param_estimate.Rd b/man/util_poisson_param_estimate.Rd
index aa3f983b..1015d134 100644
--- a/man/util_poisson_param_estimate.Rd
+++ b/man/util_poisson_param_estimate.Rd
@@ -36,7 +36,7 @@ output <- util_poisson_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 t <- rpois(50, 5)
diff --git a/man/util_poisson_stats_tbl.Rd b/man/util_poisson_stats_tbl.Rd
index 4963b57a..b6470eaf 100644
--- a/man/util_poisson_stats_tbl.Rd
+++ b/man/util_poisson_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_poisson() \%>\%
-  util_poisson_stats_tbl() \%>\%
+tidy_poisson() |>
+  util_poisson_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_t_stats_tbl.Rd b/man/util_t_stats_tbl.Rd
index c280a24c..a2517a1c 100644
--- a/man/util_t_stats_tbl.Rd
+++ b/man/util_t_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_t() \%>\%
-  util_t_stats_tbl() \%>\%
+tidy_t() |>
+  util_t_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_uniform_param_estimate.Rd b/man/util_uniform_param_estimate.Rd
index 370260cf..83f44362 100644
--- a/man/util_uniform_param_estimate.Rd
+++ b/man/util_uniform_param_estimate.Rd
@@ -36,7 +36,7 @@ output <- util_uniform_param_estimate(x)
 
 output$parameter_tbl
 
-output$combined_data_tbl \%>\%
+output$combined_data_tbl |>
   tidy_combined_autoplot()
 
 }
diff --git a/man/util_uniform_stats_tbl.Rd b/man/util_uniform_stats_tbl.Rd
index 6906dd5b..ae1554e6 100644
--- a/man/util_uniform_stats_tbl.Rd
+++ b/man/util_uniform_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_uniform() \%>\%
-  util_uniform_stats_tbl() \%>\%
+tidy_uniform() |>
+  util_uniform_stats_tbl() |>
   glimpse()
 
 }
diff --git a/man/util_weibull_stats_tbl.Rd b/man/util_weibull_stats_tbl.Rd
index 816f814c..3dd6b5db 100644
--- a/man/util_weibull_stats_tbl.Rd
+++ b/man/util_weibull_stats_tbl.Rd
@@ -23,8 +23,8 @@ passed from a \code{tidy_} distribution function.
 \examples{
 library(dplyr)
 
-tidy_weibull() \%>\%
-  util_weibull_stats_tbl() \%>\%
+tidy_weibull() |>
+  util_weibull_stats_tbl() |>
   glimpse()
 
 }