#29 improve html methods table formatting

inst/ms/aggreCAT.qmd

@@ -1611,10 +1611,13 @@ structured elicitation procedures where mathematical aggregation of
 human forecasts is required.
 
 ::: {.content-hidden unless-format="html"}
-```{r, include = TRUE, echo = FALSE}
+```{r}
 #| column: page
+#| include: true
+#| echo: false
 #| label: tbl-method-summary-table
 #| tbl-cap: "Summary of aggregation methods and functions, including data requirements and sources."
+#| results: asis
 aggreCAT:::method_summary_table %>% 
   ungroup %>% 
   # filter(str_detect(aggregator_fun_desc, "[?]",negate = TRUE)) %>% #drop Eng/CompWAgg
@@ -1634,17 +1637,20 @@ aggreCAT:::method_summary_table %>%
   tt() %>% 
   group_tt(
    i = list(
-      "AverageWAgg(): Averaged best estimates" = 1,
-      "LinearWAgg() Linearly-weighted best estimates" = 6,
-      "IntervalWAgg() Linearly-weighted best estimates, with weights influenced by interval widths" = 11,
-      "ShiftingWAgg() Weighted by judgemetns that shift most after discussion" = 17,
-      "ReasoningWAgg() Linearly-weighted best estimates, with weights constructed from supplementary reasoning data" = 22,
-      "ExtremisationWAgg() Takes the average of best-estimates and transforms it using the cumulative distribution function of a beta distribution" = 24,
-      "DistributionWAgg() Calculates the arithmetic mean of distributions created from expert judgements." = 26,
-      "BayesianWAgg() Bayesian aggregation methods with either uninformative or informative prior distributions" = 28
+      "`AverageWAgg()` *Averaged best estimates*" = 1,
+      "`LinearWAgg()` *Linearly-weighted best estimates*" = 6,
+      "`IntervalWAgg()` *Linearly-weighted best estimates, with weights influenced by interval widths*" = 11,
+      "`ShiftingWAgg()` *Weighted by judgemetns that shift most after discussion*" = 17,
+      "`ReasoningWAgg()` *Linearly-weighted best estimates, with weights constructed from supplementary reasoning data*" = 22,
+      "`ExtremisationWAgg()` *Takes the average of best-estimates and transforms it using the cumulative distribution function of a beta distribution*" = 24,
+      "`DistributionWAgg()` *Calculates the arithmetic mean of distributions created from expert judgements*" = 26,
+      "`BayesianWAgg()` *Bayesian aggregation methods with either uninformative or informative prior distributions*" = 28
     )
   ) %>% 
-  format_tt(j = c(3,4), markdown = TRUE)
+  # format_tt(j = c(3,4), markdown = TRUE) %>% 
+  print("markdown")
+  # style_tt(i = c(1,6,11,17,22,26,28), markdown = TRUE)
+  
 #TODO needs tidying up for html presentation
   
 ```