Fix R check issues

Author: schuemie

DESCRIPTION

@@ -2,7 +2,7 @@ Package: CohortMethod
 Type: Package
 Title: New-User Cohort Method with Large Scale Propensity and Outcome Models
 Version: 6.0.0
-Date: 2025-11-25
+Date: 2026-02-13
 Authors@R: c(
 	person("Martijn", "Schuemie", , "schuemie@ohdsi.org", role = c("aut", "cre")),
 	person("Marc", "Suchard", role = c("aut")),
@@ -61,7 +61,8 @@ Suggests:
 	R.utils,
 	RSQLite,
 	ResultModelManager,
-	markdown
+	markdown,
+	PSweight
 SystemRequirements: Java
 LinkingTo: Rcpp
 NeedsCompilation: yes

R/PsFunctions.R

@@ -750,16 +750,16 @@ trimByPs <- function(population, trimByPsArgs = createTrimByPsArgs(trimFraction
     if (trimByPsArgs$trimMethod %in% c("asymmetric", "reverse asymmetric")){
       # Remove overlapping patients
       bounds <- population |>
-        group_by(treatment) |>
-        summarise(minPs = min(propensityScore),
-                  maxPs = max(propensityScore),
+        group_by(.data$treatment) |>
+        summarise(minPs = min(.data$propensityScore),
+                  maxPs = max(.data$propensityScore),
                   .groups = "drop"
         )
       lower <- max(bounds$minPs)
       upper <- min(bounds$maxPs)
       population <- population |>
-        filter(propensityScore >= lower,
-               propensityScore <= upper)
+        filter(.data$propensityScore >= lower,
+               .data$propensityScore <= upper)
 
       deltaTarget <- beforeCountTarget - sum(population$treatment == 1)
       deltaComparator <- beforeCountComparator - sum(population$treatment == 0)

R/SettingsObjects.R

@@ -429,10 +429,10 @@ CreatePsArgs <- R6Class(
 #' @param trimMethod     The trimming method to be performed. Three methods are supported:
 #'
 #' - symmetric: trims all units with estimated PS outside the interval
-#' (`trimFraction`,1−`trimFraction`), following Crump et al. (2009).
+#' (`trimFraction`,1-`trimFraction`), following Crump et al. (2009).
 #' - asymmetric: removes all units not in the overlap PS range and trims the
 #' `trimFraction` target persons with the lowest propensity scores and comparator
-#' persons with the highest propensity scores, following Stürmer et al. (2010).
+#' persons with the highest propensity scores, following Sturmer et al. (2010).
 #' - reverse asymmetric: removes all units not in the overlap PS range and trims the
 #' `trimFraction` target persons with the highest propensity scores and comparator
 #' persons with the lowest propensity scores (not suggested).
@@ -445,7 +445,7 @@ CreatePsArgs <- R6Class(
 #' Crump, Richard K., V. Joseph Hotz, Guido W. Imbens, and Oscar A. Mitnik. 2009. Dealing
 #' with limited overlap in estimation of average treatment effects. Biometrika 96(1): 187-199.
 #'
-#' Stürmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured
+#' Sturmer T, Rothman KJ, Avorn J, Glynn RJ. Treatment effects in the presence of unmeasured
 #' confounding: dealing with observations in the tails of the propensity score distribution--a simulation study.
 #' Am J Epidemiol. 2010 Oct 1;172(7):843-54.
 #'

man/createTrimByPsArgs.Rd

@@ -1,7 +1,7 @@
 library(CohortMethod)
 library(testthat)
 library(pROC)
-library(PSweight)
+# library(PSweight)
 
 test_that("Simple 1-on-1 matching", {
   rowId <- 1:5
@@ -267,6 +267,7 @@ test_that("IPTW ATO", {
 })
 
 test_that("Trimming symmetric", {
+  skip_if_not_installed("PSweight")
   rowId <- 1:10000
   treatment <- rep(c(1, 1, 1, 0), 2500)
   propensityScore <- (1:10000) / 10000
@@ -275,7 +276,7 @@ test_that("Trimming symmetric", {
                                      trimMethod = "symmetric")
   result <- trimByPs(data,
                      trimByPsArgs = trimByPsArgs)
-  gold <- PStrim(data = data,
+  gold <- PSweight::PStrim(data = data,
                  zname = "treatment",
                  ps.estimate = data$propensityScore,
                  delta = 0.1)
@@ -305,6 +306,7 @@ test_that("Trimming removing an entire treatment group", {
 })
 
 test_that("Trimming symmetric", {
+  skip_if_not_installed("PSweight")
   rowId <- 1:10000
   treatment <- rep(c(1, 1, 1, 0), 2500)
   propensityScore <- (1:10000) / 10000
@@ -313,7 +315,7 @@ test_that("Trimming symmetric", {
                                      trimMethod = "symmetric")
   result <- trimByPs(data,
                      trimByPsArgs = trimByPsArgs)
-  gold <- PStrim(data = data,
+  gold <- PSweight::PStrim(data = data,
                  zname = "treatment",
                  ps.estimate = data$propensityScore,
                  delta = 0.1)