bug!: fix @imports #97 #102

R/calculate_deviation_score.R

@@ -13,6 +13,8 @@
 #' # tar_load(meta_analysis_outputs)
 #' # calculate_deviation_score(meta_analysis_outputs$data[[1]],
 #' #                           meta_analysis_outputs$MA_mod[[1]])
+#' @import dplyr
+#' @importFrom cli cli_h2
 calculate_deviation_score <- function(data, meta_analytic_model) {
   cli::cli_h2(c("Calculating absolute deviation scores from standardised effect sizes"))
 
@@ -25,7 +27,6 @@ calculate_deviation_score <- function(data, meta_analytic_model) {
   meta_analytic_mean <- meta_analytic_model$beta[[1]] # TODO should this be the fitted val from the model, or the raw score?
   # meta_analytic_se <- meta_analytic_model$se[[1]]
 
-
   out <- data %>%
     ungroup() %>%
     mutate(across(

R/filt_multivar_MA.R

@@ -10,14 +10,14 @@
 #' @return An object of class lmer.
 #'
 #' @export
-#' @importFrom rlang new_formula
-#' @importFrom rlang caller_env
-#' @importFrom rlang expr
-#' @importFrom rlang expr
+#' @importFrom rlang new_formula caller_env expr inject
 #' @importFrom lme4 lmer
-#' @importFrom pointblank test_col_vals_gte
+#' @importFrom pointblank test_col_vals_gte expect_col_exists
 #' @import dplyr
-#' @import cli
+#' @importFrom purrr list_flatten list_c
+#' @importFrom tibble enframe unite
+#' @importFrom cli cli_alert_info cli_bullets cli_h2 style_italic
+#' @importFrom glue glue
 #' @details
 #' Depending on whether enough analyses in `data_tbl` have been conducted with the `mixed_model` variable, the function will fit a model with or without the predictor `mixed_model`.
 #'