add propensity to imports and handle importing namespace

DESCRIPTION

@@ -20,6 +20,7 @@ Imports:
     dplyr,
     ggplot2,
     gtsummary (>= 2.1.0),
+    propensity (>= 0.0.0.9000),
     purrr,
     rlang,
     smd,
@@ -43,4 +44,5 @@ LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 Remotes:  
-    r-causal/tidysmd
+    r-causal/tidysmd,
+    r-causal/propensity

R/geom_qq2.R

@@ -53,13 +53,8 @@
 #'   geom_abline(intercept = 0, slope = 1, linetype = "dashed")
 #'
 #' # Compare multiple weights using long format
-#' # TODO: Remove vec_data() workaround once propensity implements vctrs methods
-#' # Extract numeric data from psw objects first
-#' nhefs_for_pivot <- nhefs_weights
-#' nhefs_for_pivot$w_ate <- vctrs::vec_data(nhefs_weights$w_ate)
-#' nhefs_for_pivot$w_att <- vctrs::vec_data(nhefs_weights$w_att)
 #' long_data <- tidyr::pivot_longer(
-#'   nhefs_for_pivot,
+#'   nhefs_weights,
 #'   cols = c(w_ate, w_att),
 #'   names_to = "weight_type",
 #'   values_to = "weight"

R/geom_roc.R

@@ -24,13 +24,8 @@
 #'   geom_abline(intercept = 0, slope = 1, linetype = "dashed")
 #'
 #' # With grouping by weight
-#' # TODO: Remove vec_data() workaround once propensity implements vctrs methods
-#' # Extract numeric data from psw objects first
-#' nhefs_for_pivot <- nhefs_weights
-#' nhefs_for_pivot$w_ate <- vctrs::vec_data(nhefs_weights$w_ate)
-#' nhefs_for_pivot$w_att <- vctrs::vec_data(nhefs_weights$w_att)
 #' long_data <- tidyr::pivot_longer(
-#'   nhefs_for_pivot,
+#'   nhefs_weights,
 #'   cols = c(w_ate, w_att),
 #'   names_to = "weight_type",
 #'   values_to = "weight"

R/utils-validation.R

@@ -17,7 +17,7 @@ validate_weights <- function(weights, n, arg_name = "weights", call = rlang::cal
   if (is.null(weights)) return(invisible(weights))
 
   # Accept both numeric vectors and psw objects from propensity package
-  is_valid_weights <- is.numeric(weights) || inherits(weights, "psw")
+  is_valid_weights <- is.numeric(weights) || propensity::is_psw(weights)
 
   if (!is_valid_weights) {
     abort(

R/zzz.R

@@ -0,0 +1,8 @@
+.onLoad <- function(libname, pkgname) {
+  # Ensure propensity's namespace is loaded so its S3 methods are registered
+  # This is needed for psw objects in nhefs_weights to work properly without
+  # users having to explicitly load propensity
+  loadNamespace("propensity")
+
+  invisible()
+}

tests/testthat/test-compute_balance.R

@@ -1506,8 +1506,8 @@ test_that("balance functions work seamlessly with psw objects from propensity pa
   data(nhefs_weights)
 
   # Verify we have psw objects in the dataset
-  expect_true(inherits(nhefs_weights$w_cat_ate, "psw"))
-  expect_true(inherits(nhefs_weights$w_cat_att_none, "psw"))
+  expect_true(propensity::is_psw(nhefs_weights$w_cat_ate))
+  expect_true(propensity::is_psw(nhefs_weights$w_cat_att_none))
 
   # Test that balance functions work directly with psw weights
   result_smd <- bal_smd(

tests/testthat/test-extract_weight_data.R

@@ -36,7 +36,7 @@ test_that("extract_weight_data extracts data from psw objects", {
   expect_type(result, "double")
 
   # Should not have psw class anymore
-  expect_false(inherits(result, "psw"))
+  expect_false(propensity::is_psw(result))
 })
 
 test_that("extract_weight_data preserves values from different psw estimands", {

tests/testthat/test-geom_qq2.R

@@ -43,14 +43,9 @@ test_that("geom_qq2 works with weights", {
 })
 
 test_that("geom_qq2 works with color aesthetic for multiple weights", {
-  # Create long format data - need to extract numeric data from psw columns first
-  # TODO: Remove vec_data() workaround once propensity implements vctrs methods
-  nhefs_for_pivot <- nhefs_weights
-  nhefs_for_pivot$w_ate <- vctrs::vec_data(nhefs_weights$w_ate)
-  nhefs_for_pivot$w_att <- vctrs::vec_data(nhefs_weights$w_att)
-
+  # Create long format data
   long_data <- tidyr::pivot_longer(
-    nhefs_for_pivot,
+    nhefs_weights,
     cols = c(w_ate, w_att),
     names_to = "weight_type",
     values_to = "weight"

tests/testthat/test-geom_roc.R

@@ -75,14 +75,9 @@ test_that("geom_roc visual regression", {
   )
 
   # Multiple groups with different weights
-  # First create long format data - need to extract numeric data from psw columns
-  # TODO: Remove vec_data() workaround once propensity implements vctrs methods
-  nhefs_for_pivot <- nhefs_weights
-  nhefs_for_pivot$w_ate <- vctrs::vec_data(nhefs_weights$w_ate)
-  nhefs_for_pivot$w_att <- vctrs::vec_data(nhefs_weights$w_att)
-
+  # Create long format data
   long_data <- tidyr::pivot_longer(
-    nhefs_for_pivot,
+    nhefs_weights,
     cols = c(w_ate, w_att),
     names_to = "weight_type",
     values_to = "weight"