Remove mentions of the getting started vignette which doesn't exist a…

R/score.R

@@ -10,9 +10,8 @@
 #' See [get_forecast_unit()] for more information on the concept of a forecast
 #' unit.
 #'
-#' For additional help and examples, check out the [Getting Started
-#' Vignette](https://epiforecasts.io/scoringutils/articles/scoringutils.html) as
-#' well as the paper [Evaluating Forecasts with scoringutils in
+#' For additional help and examples, check out the paper
+#' [Evaluating Forecasts with scoringutils in
 #' R](https://arxiv.org/abs/2205.07090).
 #' @param forecast A forecast object (a validated data.table with predicted and
 #'   observed values).
@@ -103,7 +102,7 @@ score.default <- function(forecast, metrics, ...) {
     #nolint start: keyword_quote_linter
     c(
       "!" = "The input needs to be a valid forecast object.",
-      "i" = "Please convert to `forecast` object first by calling the
+      "i" = "Please convert to a `forecast` object first by calling the
       appropriate {.fn as_forecast_<type>} function)."
     )
     #nolint end

README.Rmd

@@ -56,7 +56,9 @@ The `scoringutils` package facilitates the process of evaluating forecasts in R,
 
 The package underwent a major re-write. The most comprehensive documentation for the updated package is the [revised version](https://drive.google.com/file/d/1URaMsXmHJ1twpLpMl1sl2HW4lPuUycoj/view?usp=drive_link) of our [original](https://doi.org/10.48550/arXiv.2205.07090) `scoringutils` paper. 
 
-Another good starting point are the vignettes on [Getting started](https://epiforecasts.io/scoringutils/articles/scoringutils.html), [Details on the metrics implemented](https://epiforecasts.io/scoringutils/articles/metric-details.html) and [Scoring forecasts directly](https://epiforecasts.io/scoringutils/articles/scoring-forecasts-directly.html).
+Another good starting point are the vignettes
+<!-- vignettes on [Getting started](https://epiforecasts.io/scoringutils/articles/scoringutils.html),  -->
+[Details on the metrics implemented](https://epiforecasts.io/scoringutils/articles/metric-details.html) and [Scoring forecasts directly](https://epiforecasts.io/scoringutils/articles/scoring-forecasts-directly.html).
 
 <!-- > Nikos I. Bosse, Hugo Gruson, Anne Cori, Edwin van Leeuwen, Sebastian Funk and Sam Abbott (2022). _`Evaluating Forecasts with scoringutils in R`_. arXiv:2205.07090 <https://doi.org/10.48550/arXiv.2205.07090> -->
 
@@ -83,6 +85,7 @@ remotes::install_github("epiforecasts/scoringutils", dependencies = TRUE)
 ### Forecast types
 
 `scoringutils` currently supports scoring the following forecast types: 
+
 - `binary`: a probability for a binary (yes/no) outcome variable.
 - `point`: a forecast for a continuous or discrete outcome variable that is represented by a single number.
 - `quantile`: a probabilistic forecast for a continuous or discrete outcome variable, with the forecast distribution represented by a set of predictive quantiles.