From a02afe0ae25b1332702c2f581622a090e089bd40 Mon Sep 17 00:00:00 2001
From: Matt Dancho <mdancho@gmail.com>
Date: Mon, 29 Jul 2024 12:56:59 -0400
Subject: [PATCH] cran fix: targets missing package anchors

---
 R/data.R                        |  2 +-
 R/modeltime-accuracy.R          | 12 ++++++------
 R/modeltime-calibrate.R         |  4 ++--
 R/modeltime-forecast-plot.R     |  2 +-
 R/modeltime-residuals-plot.R    | 12 ++++++------
 R/parsnip-adam.R                |  2 +-
 R/parsnip-arima_boost.R         |  2 +-
 R/parsnip-arima_reg.R           |  2 +-
 R/parsnip-exp_smoothing.R       |  2 +-
 R/parsnip-naive_reg.R           |  2 +-
 R/parsnip-nnetar_reg.R          |  2 +-
 R/parsnip-prophet_boost.R       |  2 +-
 R/parsnip-prophet_reg.R         |  2 +-
 R/parsnip-seasonal_reg.R        |  2 +-
 R/parsnip-temporal_hierarchy.R  |  2 +-
 R/parsnip-window_reg.R          |  2 +-
 R/yardstick-metric-sets.R       | 14 +++++++-------
 man/adam_reg.Rd                 |  2 +-
 man/arima_boost.Rd              |  2 +-
 man/arima_reg.Rd                |  2 +-
 man/exp_smoothing.Rd            |  2 +-
 man/m750.Rd                     |  2 +-
 man/metric_sets.Rd              | 14 +++++++-------
 man/modeltime_accuracy.Rd       | 12 ++++++------
 man/modeltime_calibrate.Rd      |  4 ++--
 man/naive_reg.Rd                |  2 +-
 man/nnetar_reg.Rd               |  2 +-
 man/plot_modeltime_forecast.Rd  |  2 +-
 man/plot_modeltime_residuals.Rd | 12 ++++++------
 man/prophet_boost.Rd            |  2 +-
 man/prophet_reg.Rd              |  2 +-
 man/seasonal_reg.Rd             |  2 +-
 man/temporal_hierarchy.Rd       |  2 +-
 man/window_reg.Rd               |  2 +-
 34 files changed, 68 insertions(+), 68 deletions(-)

diff --git a/R/data.R b/R/data.R
index 53cbb439..facc8b53 100644
--- a/R/data.R
+++ b/R/data.R
@@ -15,7 +15,7 @@
 #' m750
 #'
 #' @source
-#'  - [M4 Competition Website](https://mofc.unic.ac.cy/m4/)
+#'  - M4 Competition Website: https://www.unic.ac.cy/iff/research/forecasting/m-competitions/m4/
 #'
 "m750"
 
diff --git a/R/modeltime-accuracy.R b/R/modeltime-accuracy.R
index 88fc0019..e1e19b52 100644
--- a/R/modeltime-accuracy.R
+++ b/R/modeltime-accuracy.R
@@ -27,12 +27,12 @@
 #'
 #' The following accuracy metrics are included by default via [default_forecast_accuracy_metric_set()]:
 #'
-#' - MAE - Mean absolute error, [mae()]
-#' - MAPE - Mean absolute percentage error, [mape()]
-#' - MASE  - Mean absolute scaled error, [mase()]
-#' - SMAPE - Symmetric mean absolute percentage error, [smape()]
-#' - RMSE  - Root mean squared error, [rmse()]
-#' - RSQ   - R-squared, [rsq()]
+#' - MAE - Mean absolute error, `mae()`
+#' - MAPE - Mean absolute percentage error, `mape()`
+#' - MASE  - Mean absolute scaled error, `mase()`
+#' - SMAPE - Symmetric mean absolute percentage error, `smape()`
+#' - RMSE  - Root mean squared error, `rmse()`
+#' - RSQ   - R-squared, `rsq()`
 #'
 #'
 #'
diff --git a/R/modeltime-calibrate.R b/R/modeltime-calibrate.R
index 233620b9..0a763f04 100644
--- a/R/modeltime-calibrate.R
+++ b/R/modeltime-calibrate.R
@@ -7,8 +7,8 @@
 #'
 #' @param object A fitted model object that is either:
 #' 1. A modeltime table that has been created using [modeltime_table()]
-#' 2. A workflow that has been fit by [fit.workflow()] or
-#' 3. A parsnip model that has been fit using [fit.model_spec()]
+#' 2. A workflow that has been fit by `fit.workflow()` or
+#' 3. A parsnip model that has been fit using `fit.model_spec()`
 #' @param new_data A test data set `tibble` containing future information (timestamps and actual values).
 #' @param id A quoted column name containing an identifier column identifying time series that are grouped.
 #' @param quiet Hide errors (`TRUE`, the default), or display them as they occur?
diff --git a/R/modeltime-forecast-plot.R b/R/modeltime-forecast-plot.R
index 08b3afa8..efef9e5a 100644
--- a/R/modeltime-forecast-plot.R
+++ b/R/modeltime-forecast-plot.R
@@ -1,6 +1,6 @@
 #' Interactive Forecast Visualization
 #'
-#' This is a wrapper for [plot_time_series()] that generates an interactive (`plotly`) or static
+#' This is a wrapper for `timetk::plot_time_series()` that generates an interactive (`plotly`) or static
 #' (`ggplot2`) plot with the forecasted data.
 #'
 #' @inheritParams timetk::plot_time_series
diff --git a/R/modeltime-residuals-plot.R b/R/modeltime-residuals-plot.R
index 705954fb..2e588c93 100644
--- a/R/modeltime-residuals-plot.R
+++ b/R/modeltime-residuals-plot.R
@@ -1,9 +1,9 @@
 #' Interactive Residuals Visualization
 #'
 #' This is a wrapper for examining residuals using:
-#' - Time Plot: [plot_time_series()]
-#' - ACF Plot: [plot_acf_diagnostics()]
-#' - Seasonality Plot: [plot_seasonal_diagnostics()]
+#' - Time Plot: `timetk::plot_time_series()`
+#' - ACF Plot: `timetk::plot_acf_diagnostics()`
+#' - Seasonality Plot: `timetk::plot_seasonal_diagnostics()`
 #'
 #'
 #' @inheritParams timetk::plot_time_series
@@ -13,9 +13,9 @@
 #'  Can save space with long model descriptions.
 #' @param .legend_max_width Numeric. The width of truncation to apply to the legend text.
 #' @param ... Additional arguments passed to:
-#' - Time Plot: [plot_time_series()]
-#' - ACF Plot: [plot_acf_diagnostics()]
-#' - Seasonality Plot: [plot_seasonal_diagnostics()]
+#' - Time Plot: `timetk::plot_time_series()`
+#' - ACF Plot: `timetk::plot_acf_diagnostics()`
+#' - Seasonality Plot: `timetk::plot_seasonal_diagnostics()`
 #'
 #' @return A static `ggplot2` plot or an interactive `plotly` plot containing residuals vs time
 #'
diff --git a/R/parsnip-adam.R b/R/parsnip-adam.R
index 862cc320..f2677e53 100644
--- a/R/parsnip-adam.R
+++ b/R/parsnip-adam.R
@@ -165,7 +165,7 @@
 #'
 #'  Note that date or date-time class values are excluded from `xreg`.
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examplesIf rlang::is_installed("smooth")
 #'
diff --git a/R/parsnip-arima_boost.R b/R/parsnip-arima_boost.R
index eff05848..1419d344 100644
--- a/R/parsnip-arima_boost.R
+++ b/R/parsnip-arima_boost.R
@@ -193,7 +193,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-arima_reg.R b/R/parsnip-arima_reg.R
index 6dec2e45..e126373a 100644
--- a/R/parsnip-arima_reg.R
+++ b/R/parsnip-arima_reg.R
@@ -162,7 +162,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-exp_smoothing.R b/R/parsnip-exp_smoothing.R
index 4d71dd75..31a8bc33 100644
--- a/R/parsnip-exp_smoothing.R
+++ b/R/parsnip-exp_smoothing.R
@@ -186,7 +186,7 @@
 #'  Note that date or date-time class values are excluded from `xreg`.
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examplesIf rlang::is_installed("smooth")
 #' library(dplyr)
diff --git a/R/parsnip-naive_reg.R b/R/parsnip-naive_reg.R
index c5dea5a4..b4a5c0b1 100644
--- a/R/parsnip-naive_reg.R
+++ b/R/parsnip-naive_reg.R
@@ -93,7 +93,7 @@
 #' These models are univariate. No xregs are used in the modeling process.
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-nnetar_reg.R b/R/parsnip-nnetar_reg.R
index 6691125d..a437c60f 100644
--- a/R/parsnip-nnetar_reg.R
+++ b/R/parsnip-nnetar_reg.R
@@ -126,7 +126,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-prophet_boost.R b/R/parsnip-prophet_boost.R
index 0064b746..70e88c41 100644
--- a/R/parsnip-prophet_boost.R
+++ b/R/parsnip-prophet_boost.R
@@ -191,7 +191,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-prophet_reg.R b/R/parsnip-prophet_reg.R
index 7512cb20..0d21add9 100644
--- a/R/parsnip-prophet_reg.R
+++ b/R/parsnip-prophet_reg.R
@@ -175,7 +175,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-seasonal_reg.R b/R/parsnip-seasonal_reg.R
index 3e4c8b47..2a4a23f8 100644
--- a/R/parsnip-seasonal_reg.R
+++ b/R/parsnip-seasonal_reg.R
@@ -133,7 +133,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/parsnip-temporal_hierarchy.R b/R/parsnip-temporal_hierarchy.R
index 48b5a175..1c5ddbf1 100644
--- a/R/parsnip-temporal_hierarchy.R
+++ b/R/parsnip-temporal_hierarchy.R
@@ -96,7 +96,7 @@
 #' This model is not set up for use with exogenous regressors.
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examplesIf rlang::is_installed("thief")
 #' library(dplyr)
diff --git a/R/parsnip-window_reg.R b/R/parsnip-window_reg.R
index b8012d1d..a2227b0e 100644
--- a/R/parsnip-window_reg.R
+++ b/R/parsnip-window_reg.R
@@ -103,7 +103,7 @@
 #' These models are univariate. No xregs are used in the modeling process.
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)
diff --git a/R/yardstick-metric-sets.R b/R/yardstick-metric-sets.R
index 4ec7959c..7e26a2fc 100644
--- a/R/yardstick-metric-sets.R
+++ b/R/yardstick-metric-sets.R
@@ -3,7 +3,7 @@
 #' Forecast Accuracy Metrics Sets
 #'
 #'
-#' This is a wrapper for [metric_set()] with several common forecast / regression
+#' This is a wrapper for `metric_set()` with several common forecast / regression
 #' accuracy metrics included. These are the default time series accuracy
 #' metrics used with [modeltime_accuracy()].
 #'
@@ -16,12 +16,12 @@
 #' The primary purpose is to use the default accuracy metrics to calculate the following
 #' forecast accuracy metrics using [modeltime_accuracy()]:
 #'
-#' - MAE   - Mean absolute error, [mae()]
-#' - MAPE  - Mean absolute percentage error, [mape()]
-#' - MASE  - Mean absolute scaled error, [mase()]
-#' - SMAPE - Symmetric mean absolute percentage error, [smape()]
-#' - RMSE  - Root mean squared error, [rmse()]
-#' - RSQ   - R-squared, [rsq()]
+#' - MAE   - Mean absolute error, `mae()`
+#' - MAPE  - Mean absolute percentage error, `mape()`
+#' - MASE  - Mean absolute scaled error, `mase()`
+#' - SMAPE - Symmetric mean absolute percentage error, `smape()`
+#' - RMSE  - Root mean squared error, `rmse()`
+#' - RSQ   - R-squared, `rsq()`
 #'
 #' Adding additional metrics is possible via `...`.
 #'
diff --git a/man/adam_reg.Rd b/man/adam_reg.Rd
index 2e326ff5..48b0b32a 100644
--- a/man/adam_reg.Rd
+++ b/man/adam_reg.Rd
@@ -288,5 +288,5 @@ model_fit
 \dontshow{\}) # examplesIf}
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/arima_boost.Rd b/man/arima_boost.Rd
index 8f90b74c..f51d4a6d 100644
--- a/man/arima_boost.Rd
+++ b/man/arima_boost.Rd
@@ -304,5 +304,5 @@ model_fit_boosted
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/arima_reg.Rd b/man/arima_reg.Rd
index c51bdb21..fda10d79 100644
--- a/man/arima_reg.Rd
+++ b/man/arima_reg.Rd
@@ -248,5 +248,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/exp_smoothing.Rd b/man/exp_smoothing.Rd
index 9c7668dd..342da671 100644
--- a/man/exp_smoothing.Rd
+++ b/man/exp_smoothing.Rd
@@ -336,5 +336,5 @@ model_fit
 \dontshow{\}) # examplesIf}
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/m750.Rd b/man/m750.Rd
index ded136af..52844b6d 100644
--- a/man/m750.Rd
+++ b/man/m750.Rd
@@ -14,7 +14,7 @@ A \code{tibble} with 306 rows and 3 variables:
 }
 \source{
 \itemize{
-\item \href{https://mofc.unic.ac.cy/m4/}{M4 Competition Website}
+\item M4 Competition Website: https://www.unic.ac.cy/iff/research/forecasting/m-competitions/m4/
 }
 }
 \usage{
diff --git a/man/metric_sets.Rd b/man/metric_sets.Rd
index a1b12756..ec4c3e02 100644
--- a/man/metric_sets.Rd
+++ b/man/metric_sets.Rd
@@ -14,7 +14,7 @@ extended_forecast_accuracy_metric_set(...)
 \item{...}{Add additional \code{yardstick} metrics}
 }
 \description{
-This is a wrapper for \code{\link[=metric_set]{metric_set()}} with several common forecast / regression
+This is a wrapper for \code{metric_set()} with several common forecast / regression
 accuracy metrics included. These are the default time series accuracy
 metrics used with \code{\link[=modeltime_accuracy]{modeltime_accuracy()}}.
 }
@@ -22,12 +22,12 @@ metrics used with \code{\link[=modeltime_accuracy]{modeltime_accuracy()}}.
 The primary purpose is to use the default accuracy metrics to calculate the following
 forecast accuracy metrics using \code{\link[=modeltime_accuracy]{modeltime_accuracy()}}:
 \itemize{
-\item MAE   - Mean absolute error, \code{\link[=mae]{mae()}}
-\item MAPE  - Mean absolute percentage error, \code{\link[=mape]{mape()}}
-\item MASE  - Mean absolute scaled error, \code{\link[=mase]{mase()}}
-\item SMAPE - Symmetric mean absolute percentage error, \code{\link[=smape]{smape()}}
-\item RMSE  - Root mean squared error, \code{\link[=rmse]{rmse()}}
-\item RSQ   - R-squared, \code{\link[=rsq]{rsq()}}
+\item MAE   - Mean absolute error, \code{mae()}
+\item MAPE  - Mean absolute percentage error, \code{mape()}
+\item MASE  - Mean absolute scaled error, \code{mase()}
+\item SMAPE - Symmetric mean absolute percentage error, \code{smape()}
+\item RMSE  - Root mean squared error, \code{rmse()}
+\item RSQ   - R-squared, \code{rsq()}
 }
 
 Adding additional metrics is possible via \code{...}.
diff --git a/man/modeltime_accuracy.Rd b/man/modeltime_accuracy.Rd
index e369f01c..531356aa 100644
--- a/man/modeltime_accuracy.Rd
+++ b/man/modeltime_accuracy.Rd
@@ -43,12 +43,12 @@ calculations from a fitted \code{workflow} (trained workflow) or \code{model_fit
 \details{
 The following accuracy metrics are included by default via \code{\link[=default_forecast_accuracy_metric_set]{default_forecast_accuracy_metric_set()}}:
 \itemize{
-\item MAE - Mean absolute error, \code{\link[=mae]{mae()}}
-\item MAPE - Mean absolute percentage error, \code{\link[=mape]{mape()}}
-\item MASE  - Mean absolute scaled error, \code{\link[=mase]{mase()}}
-\item SMAPE - Symmetric mean absolute percentage error, \code{\link[=smape]{smape()}}
-\item RMSE  - Root mean squared error, \code{\link[=rmse]{rmse()}}
-\item RSQ   - R-squared, \code{\link[=rsq]{rsq()}}
+\item MAE - Mean absolute error, \code{mae()}
+\item MAPE - Mean absolute percentage error, \code{mape()}
+\item MASE  - Mean absolute scaled error, \code{mase()}
+\item SMAPE - Symmetric mean absolute percentage error, \code{smape()}
+\item RMSE  - Root mean squared error, \code{rmse()}
+\item RSQ   - R-squared, \code{rsq()}
 }
 }
 \examples{
diff --git a/man/modeltime_calibrate.Rd b/man/modeltime_calibrate.Rd
index 1b4d0f60..07daad35 100644
--- a/man/modeltime_calibrate.Rd
+++ b/man/modeltime_calibrate.Rd
@@ -10,8 +10,8 @@ modeltime_calibrate(object, new_data, id = NULL, quiet = TRUE, ...)
 \item{object}{A fitted model object that is either:
 \enumerate{
 \item A modeltime table that has been created using \code{\link[=modeltime_table]{modeltime_table()}}
-\item A workflow that has been fit by \code{\link[=fit.workflow]{fit.workflow()}} or
-\item A parsnip model that has been fit using \code{\link[=fit.model_spec]{fit.model_spec()}}
+\item A workflow that has been fit by \code{fit.workflow()} or
+\item A parsnip model that has been fit using \code{fit.model_spec()}
 }}
 
 \item{new_data}{A test data set \code{tibble} containing future information (timestamps and actual values).}
diff --git a/man/naive_reg.Rd b/man/naive_reg.Rd
index 70ba6349..43f617fb 100644
--- a/man/naive_reg.Rd
+++ b/man/naive_reg.Rd
@@ -148,5 +148,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/nnetar_reg.Rd b/man/nnetar_reg.Rd
index 6b3c3cc4..606bdfb6 100644
--- a/man/nnetar_reg.Rd
+++ b/man/nnetar_reg.Rd
@@ -191,5 +191,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/plot_modeltime_forecast.Rd b/man/plot_modeltime_forecast.Rd
index 84d3e19b..14fc26ed 100644
--- a/man/plot_modeltime_forecast.Rd
+++ b/man/plot_modeltime_forecast.Rd
@@ -80,7 +80,7 @@ The only parameters that cannot be passed are:
 A static \code{ggplot2} plot or an interactive \code{plotly} plot containing a forecast
 }
 \description{
-This is a wrapper for \code{\link[=plot_time_series]{plot_time_series()}} that generates an interactive (\code{plotly}) or static
+This is a wrapper for \code{timetk::plot_time_series()} that generates an interactive (\code{plotly}) or static
 (\code{ggplot2}) plot with the forecasted data.
 }
 \examples{
diff --git a/man/plot_modeltime_residuals.Rd b/man/plot_modeltime_residuals.Rd
index d40adad9..9b11ae23 100644
--- a/man/plot_modeltime_residuals.Rd
+++ b/man/plot_modeltime_residuals.Rd
@@ -43,9 +43,9 @@ Can save space with long model descriptions.}
 
 \item{...}{Additional arguments passed to:
 \itemize{
-\item Time Plot: \code{\link[=plot_time_series]{plot_time_series()}}
-\item ACF Plot: \code{\link[=plot_acf_diagnostics]{plot_acf_diagnostics()}}
-\item Seasonality Plot: \code{\link[=plot_seasonal_diagnostics]{plot_seasonal_diagnostics()}}
+\item Time Plot: \code{timetk::plot_time_series()}
+\item ACF Plot: \code{timetk::plot_acf_diagnostics()}
+\item Seasonality Plot: \code{timetk::plot_seasonal_diagnostics()}
 }}
 }
 \value{
@@ -54,9 +54,9 @@ A static \code{ggplot2} plot or an interactive \code{plotly} plot containing res
 \description{
 This is a wrapper for examining residuals using:
 \itemize{
-\item Time Plot: \code{\link[=plot_time_series]{plot_time_series()}}
-\item ACF Plot: \code{\link[=plot_acf_diagnostics]{plot_acf_diagnostics()}}
-\item Seasonality Plot: \code{\link[=plot_seasonal_diagnostics]{plot_seasonal_diagnostics()}}
+\item Time Plot: \code{timetk::plot_time_series()}
+\item ACF Plot: \code{timetk::plot_acf_diagnostics()}
+\item Seasonality Plot: \code{timetk::plot_seasonal_diagnostics()}
 }
 }
 \examples{
diff --git a/man/prophet_boost.Rd b/man/prophet_boost.Rd
index 634441ec..87c399b0 100644
--- a/man/prophet_boost.Rd
+++ b/man/prophet_boost.Rd
@@ -324,5 +324,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/prophet_reg.Rd b/man/prophet_reg.Rd
index 77ca212f..740e4d4d 100644
--- a/man/prophet_reg.Rd
+++ b/man/prophet_reg.Rd
@@ -248,5 +248,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/seasonal_reg.Rd b/man/seasonal_reg.Rd
index 95e40e4e..d9d440f3 100644
--- a/man/seasonal_reg.Rd
+++ b/man/seasonal_reg.Rd
@@ -192,5 +192,5 @@ model_fit
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/temporal_hierarchy.Rd b/man/temporal_hierarchy.Rd
index 9f699216..c94b870d 100644
--- a/man/temporal_hierarchy.Rd
+++ b/man/temporal_hierarchy.Rd
@@ -146,5 +146,5 @@ model_fit
 }
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }
diff --git a/man/window_reg.Rd b/man/window_reg.Rd
index 8bb83ed3..93c7b6ce 100644
--- a/man/window_reg.Rd
+++ b/man/window_reg.Rd
@@ -205,5 +205,5 @@ predict(model_fit, testing(splits))
 
 }
 \seealso{
-\code{\link[=fit.model_spec]{fit.model_spec()}}, \code{\link[=set_engine]{set_engine()}}
+\code{fit.model_spec()}, \code{set_engine()}
 }