https://github.com/easystats/easystatstemplate/issues/9

R/get_variances.R

@@ -72,17 +72,20 @@
 #' which depends on the model family.
 #' - **Gaussian:** For Gaussian models, it is
 #'   \ifelse{html}{\out{&sigma;<sup>2</sup>}}{\eqn{\sigma^2}} (i.e. `sigma(model)^2`).
-#' - **Bernoulli:** For models with binary outcome, it is \eqn{\pi^2 / 3} for
-#'   logit-link, `1` for probit-link, and \eqn{\pi^2 / 6} for cloglog-links.
+#' - **Bernoulli:** For models with binary outcome, it is
+#'   \ifelse{html}{\out{&pi;<sup>2</sup> / 3}}{\eqn{\pi^2 / 3}} for logit-link,
+#'   `1` for probit-link, and \ifelse{html}{\out{&pi;<sup>2</sup> / 6}}{\eqn{\pi^2 / 6}}
+#'   for cloglog-links.
 #' - **Binomial:** For other binomial models, the distribution-specific variance
 #'   for Bernoulli models is used, divided by a weighting factor based on the
 #'   number of trials and successes.
 #' - **Gamma:** Models from Gamma-families use \eqn{\mu^2} (as obtained from
 #'   `family$variance()`).
 #' - For all other models, the distribution-specific variance is by default
-#'   based on lognormal approximation, \eqn{log(1 + var(x) / \mu^2)} (see
-#'   _Nakagawa et al. 2017_). Other approximation methods can be specified with
-#'   the `approximation` argument.
+#'   based on lognormal approximation,
+#'   \ifelse{html}{\out{log(1 + var(x) / &mu;<sup>2</sup>)}}{\eqn{log(1 + var(x) / \mu^2)}}
+#'   (see _Nakagawa et al. 2017_). Other approximation methods can be specified
+#'   with the `approximation` argument.
 #' - **Zero-inflation models:** The expected variance of a zero-inflated model
 #'   is computed according to _Zuur et al. 2012, p277_.
 #'