Dont test some examples

R/fit.R

@@ -410,6 +410,7 @@ NULL
 #' )
 #' fit
 #'
+#' \donttest{
 #' # Make the fields AR1:
 #' fit <- sdmTMB(
 #'   density ~ s(depth),
@@ -547,6 +548,7 @@ NULL
 #'   family = tweedie()
 #' )
 #' fit
+#' }
 
 sdmTMB <- function(
   formula,

R/mesh.R

@@ -41,6 +41,7 @@
 #' mesh <- make_mesh(pcod, c("X", "Y"), n_knots = 50, type = "kmeans")
 #' plot(mesh)
 #'
+#' \donttest{
 #' # But, it's better to develop more tailored meshes:
 #'
 #' # Pass arguments via '...' to fmesher::fm_mesh_2d_inla():
@@ -73,6 +74,7 @@
 #' )
 #' mesh <- make_mesh(pcod, c("X", "Y"), mesh = inla_mesh)
 #' plot(mesh)
+#' }
 
 make_mesh <- function(data, xy_cols,
                       type = c("kmeans", "cutoff", "cutoff_search"),

R/predict.R

@@ -123,6 +123,7 @@
 #' qcs_grid_2011 <- replicate_df(qcs_grid, "year", unique(pcod_2011$year))
 #' predictions <- predict(m, newdata = qcs_grid_2011)
 #'
+#' \donttest{
 #' # A short function for plotting our predictions:
 #' plot_map <- function(dat, column = est) {
 #'   ggplot(dat, aes(X, Y, fill = {{ column }})) +
@@ -237,6 +238,7 @@
 #' plot_map(p, exp(est)) +
 #'   ggtitle("Prediction (fixed effects + all random effects)") +
 #'   scale_fill_viridis_c(trans = "sqrt")
+#' }
 
 predict.sdmTMB <- function(object, newdata = NULL,
   type = c("link", "response"),