Fix issues with docs

.gitignore

@@ -22,3 +22,4 @@ docs
 README.html
 /doc/
 /Meta/
+scoringutils.Rcheck/*

R/documentation-templates.R

@@ -94,7 +94,8 @@ NULL
 #' \if{html}{
 #'   \out{<div style="text-align: left">}
 #'   \figure{metrics-sample.png}{options: style="width:750px;max-width:100\%;"}
-#'   \out{</div>}
+#'   \out{</div><p>}
+#'   Overview of required input format for sample-based forecasts
 #' }
 #' \if{latex}{
 #'   \figure{metrics-sample.png}
@@ -108,7 +109,8 @@ NULL
 #' \if{html}{
 #'   \out{<div style="text-align: left">}
 #'   \figure{metrics-binary-point.png}{options: style="width:750px;max-width:100\%;"}
-#'   \out{</div>}
+#'   \out{</div><p>}
+#'   Overview of required input format for binary and point forecasts
 #' }
 #' \if{latex}{
 #'   \figure{metrics-binary-point.png}
@@ -122,7 +124,8 @@ NULL
 #' \if{html}{
 #'   \out{<div style="text-align: left">}
 #'   \figure{metrics-quantile.png}{options: style="width:750px;max-width:100\%;"}
-#'   \out{</div>}
+#'   \out{</div><p>}
+#'   Overview of required input format for quantile-based forecasts
 #' }
 #' \if{latex}{
 #'   \figure{metrics-quantile.png}
@@ -136,7 +139,8 @@ NULL
 #' \if{html}{
 #'   \out{<div style="text-align: left">}
 #'   \figure{metrics-nominal.png}{options: style="width:750px;max-width:100\%;"}
-#'   \out{</div>}
+#'   \out{</div><p>}
+#'   Overview of required input format for nominal forecasts
 #' }
 #' \if{latex}{
 #'   \figure{metrics-nominal.png}

R/metrics-quantile.R

@@ -560,7 +560,7 @@ interpolate_median <- function(predicted, quantile_level) {
 #'
 #' Compute the absolute error of the median calculated as
 #' \deqn{
-#'   |observed - median prediction|
+#'   |\text{observed} - \text{median prediction}|
 #' }
 #' The median prediction is the predicted value for which quantile_level == 0.5.
 #' The function requires 0.5 to be among the quantile levels in `quantile_level`.

R/metrics-sample.R

@@ -120,7 +120,7 @@ bias_sample <- function(observed, predicted) {
 #' @description
 #' Absolute error of the median calculated as
 #' \deqn{
-#'   |observed - median\_prediction|
+#'   |\text{observed} - \text{median prediction}|
 #' }
 #' where the median prediction is calculated as the median of the predictive
 #' samples.
@@ -465,14 +465,14 @@ mad_sample <- function(observed = NULL, predicted, ...) {
 #' predictions,
 #'
 #' \deqn{
-#    \bar{F}(u) = \frac{i = 1}{n} \sum_{i=1}^{n} F^{(i)}(u)
+#'    \bar{F}(u) = \frac{i = 1}{n} \sum_{i=1}^{n} F^{(i)}(u)
 #' }
 #'
 #' And calculating the value at each bin between quantile \eqn{q_i} and quantile
 #' \eqn{q_{i + 1}} as
 #'
 #' \deqn{
-#    \bar{F}(q_i) - \bar{F}(q_{i + 1})
+#'    \bar{F}(q_i) - \bar{F}(q_{i + 1})
 #' }
 #'
 #' Alternatively, a randomised PIT can be used instead. In this case, the PIT is
@@ -481,7 +481,7 @@ mad_sample <- function(observed = NULL, predicted, ...) {
 #' }
 #'
 #' where \eqn{v} is standard uniform and independent of \eqn{k}. The values of
-#' the PIT histogram are then calculated by binning the $u_t$ values as above.
+#' the PIT histogram are then calculated by binning the \eqn{u_t} values as above.
 #'
 #' @param quantiles A vector of quantiles between which to calculate the PIT.
 #' @param integers How to handle integer forecasts (count data). This is based

R/pairwise-comparisons.R

@@ -14,12 +14,12 @@
 #' The output of the pairwise comparisons is a set of mean score ratios,
 #' relative skill scores and p-values.
 #'
-#' The following illustrates the pairwise comparison process:
-#'
 #' \if{html}{
 #'   \out{<div style="text-align: left">}
 #'   \figure{pairwise-illustration.png}{options: style="width:750px;max-width:100\%;"}
-#'   \out{</div>}
+#'   \out{</div><p>}
+#'   Illustration of the pairwise comparison process.
+#'
 #' }
 #' \if{latex}{
 #'   \figure{pairwise-illustration.png}