cran fix: targets missing package

anchors

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"
 

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()`
 #'
 #'
 #'

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?

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

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
 #'

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")
 #'

R/parsnip-arima_boost.R

@@ -193,7 +193,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

R/parsnip-arima_reg.R

@@ -162,7 +162,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

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)

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)

R/parsnip-nnetar_reg.R

@@ -126,7 +126,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

R/parsnip-prophet_boost.R

@@ -191,7 +191,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

R/parsnip-prophet_reg.R

@@ -175,7 +175,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

R/parsnip-seasonal_reg.R

@@ -133,7 +133,7 @@
 #'
 #'
 #'
-#' @seealso [fit.model_spec()], [set_engine()]
+#' @seealso `fit.model_spec()`, `set_engine()`
 #'
 #' @examples
 #' library(dplyr)

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)

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)

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 `...`.
 #'