Merge branch 'main' into fix-lint-issues

DESCRIPTION

@@ -18,7 +18,10 @@ Authors@R: c(
            comment = c(ORCID = "0000-0002-0639-3728")),
     person("Anna", "Krystalli", , "annakrystalli@googlemail.com", 
            role = c("ctb"),
-           comment = c(ORCID = "0000-0002-2378-4915")))
+           comment = c(ORCID = "0000-0002-2378-4915")),
+    person("Nicholas G.", "Reich", email = "nick@umass.edu",
+           role = c("ctb"),
+           comment = c(ORCID = "0000-0003-3503-9899")))
 Description: Functions for combining model outputs (e.g. predictions or
     estimates) from multiple models into an aggregated ensemble model output.
 License: MIT + file LICENSE
@@ -46,6 +49,7 @@ Imports:
     magrittr,
     matrixStats,
     purrr,
+    rlang,
     tidyr,
     tidyselect
 Remotes:

NAMESPACE

@@ -4,3 +4,4 @@ export("%>%")
 export(linear_pool)
 export(simple_ensemble)
 importFrom(magrittr,"%>%")
+importFrom(rlang,.data)

R/linear_pool.R

@@ -80,6 +80,8 @@
 #' all.equal(lp_from_component_qs$value, lp_qs, tolerance = 1e-3,
 #'           check.attributes=FALSE)
 #'
+#' @importFrom rlang .data
+
 linear_pool <- function(model_outputs, weights = NULL,
                         weights_col_name = "weight",
                         model_id = "hub-ensemble",
@@ -100,7 +102,7 @@ linear_pool <- function(model_outputs, weights = NULL,
 
   # calculate linear opinion pool for different types
   ensemble_model_outputs <- model_outputs_validated |>
-    dplyr::group_split(output_type) |>
+    dplyr::group_split(.data$output_type) |>
     purrr::map_dfr(.f = function(split_outputs) {
       type <- split_outputs$output_type[1]
       if (type %in% c("mean", "cdf", "pmf")) {

R/linear_pool_quantile.R

@@ -36,6 +36,7 @@
 #'     3. Collect the samples from all component models and extract the desired quantiles.
 #'   Steps 1 and 2 in this process are performed by `distfromq::make_q_fun`.
 #' @return a `model_out_tbl` object of ensemble predictions for the `quantile` output type.
+#' @importFrom rlang .data
 
 linear_pool_quantile <- function(model_outputs, weights = NULL,
                                  weights_col_name = "weight",

R/validate_output_type_ids.R

@@ -14,15 +14,15 @@
 #' @return no return value
 #'
 #' @noRd
-#'
+#' @importFrom rlang .data
 
 validate_output_type_ids <- function(model_outputs, task_id_cols) {
   same_output_id <- model_outputs |>
-    dplyr::filter(output_type %in% c("cdf", "pmf", "quantile")) |>
-    dplyr::group_by(model_id, dplyr::across(dplyr::all_of(task_id_cols)), output_type) |>
-    dplyr::summarize(output_type_id_list=list(sort(output_type_id))) |>
+    dplyr::filter(.data$output_type %in% c("cdf", "pmf", "quantile")) |>
+    dplyr::group_by(.data$model_id, dplyr::across(dplyr::all_of(task_id_cols)), .data$output_type) |>
+    dplyr::summarize(output_type_id_list=list(sort(.data$output_type_id))) |>
     dplyr::ungroup() |>
-    dplyr::group_split(dplyr::across(dplyr::all_of(task_id_cols)), output_type) |>
+    dplyr::group_split(dplyr::across(dplyr::all_of(task_id_cols)), .data$output_type) |>
     purrr::map(.f = function(split_outputs) {
       length(unique(split_outputs$output_type_id_list)) == 1
     }) |>