Update npvp name

koenderks · Dec 9, 2024 · 0fb2a8d · 0fb2a8d
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: jfa
 Title: Statistical Methods for Auditing
 Version: 0.7.3
-Date: 2024-12-02
+Date: 2024-12-09
 Authors@R: c(
     person("Koen", "Derks", email = "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = '0000-0002-5533-9349')),

diff --git a/tests/testthat/test-fairness-selection.R b/tests/testthat/test-fairness-selection.R
@@ -32,7 +32,7 @@ test_that(desc = "Validation of fairness selection", {
   # Accuracy parity
   outcome <- fairness_selection(q1 = 1, q2 = 1, q3 = 3)
   expect_equal(outcome$measure, "ap")
-  # Negative predictive rate parity
+  # Negative predictive value parity
   outcome <- fairness_selection(q1 = 1, q2 = 1, q3 = 2, q4 = 2)
   expect_equal(outcome$measure, "npvp")
   # Specificity parity

diff --git a/vignettes/articles/model-fairness.Rmd b/vignettes/articles/model-fairness.Rmd
@@ -473,7 +473,7 @@ tailored to a specific context and dataset by answering the questions in the
 developed decision-making workflow. The fairness measure that can be selected 
 include disparate impact, equalized odds, false positive rate parity, false 
 negative rate parity, predictive rate parity, equal opportunity, specificity 
-parity, negative predictive rate parity, accuracy parity [@castelnovo_2022;
+parity, negative predictive value parity, accuracy parity [@castelnovo_2022;
 @feldman_2015; @friedler_2019; @hardt_2016, @verma_2018]. After answering the 
 questions in the decision-making workflow and selecting the fairness measure to 
 apply, a graphical representation of the followed path can be created based