diff --git a/papers/JOSE/paper.Rmd b/papers/JOSE/paper.Rmd
index d2b4dbfe7..0351d0509 100644
--- a/papers/JOSE/paper.Rmd
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@@ -105,7 +105,7 @@ library(datawizard)
 
 # Summary
 
-Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. In this paper, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude with recommendations on the handling of outliers: the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
+Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
 
 # Statement of Need
 
diff --git a/papers/JOSE/paper.log b/papers/JOSE/paper.log
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+This is XeTeX, Version 3.141592653-2.6-0.999995 (MiKTeX 23.5) (preloaded format=xelatex 2023.5.20)  27 SEP 2023 20:12
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 []\TU/lmr/m/n/10 Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-p
 ositive psy-
  []
 
 
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 \TU/lmr/m/n/10 chology: Undisclosed flexibility in data collection and analysis
  allows pre-
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 \TU/lmr/m/n/10 senting anything as significant. \TU/lmr/m/it/10 Psychological S
 cience\TU/lmr/m/n/10 , \TU/lmr/m/it/10 22\TU/lmr/m/n/10 (11), 1359–1366.
  []
diff --git a/papers/JOSE/paper.md b/papers/JOSE/paper.md
index 983f9824d..cc4deedbd 100644
--- a/papers/JOSE/paper.md
+++ b/papers/JOSE/paper.md
@@ -93,7 +93,7 @@ csl: apa.csl
 
 # Summary
 
-Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. In this paper, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude with recommendations on the handling of outliers: the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
+Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. Here, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude by reviewing the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
 
 # Statement of Need
 
diff --git a/papers/JOSE/paper.pdf b/papers/JOSE/paper.pdf
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diff --git a/papers/JOSE/paper_files/figure-latex/model-1.pdf b/papers/JOSE/paper_files/figure-latex/model-1.pdf
index cc65121f5..3ef02ecb9 100644
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diff --git a/vignettes/check_outliers.Rmd b/vignettes/check_outliers.Rmd
index 804be2432..b4fe221ed 100644
--- a/vignettes/check_outliers.Rmd
+++ b/vignettes/check_outliers.Rmd
@@ -46,7 +46,7 @@ if (can_evaluate) {
 
 # Summary
 
-Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. In this paper, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude with recommendations on the handling of outliers: the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
+Beyond the challenge of keeping up-to-date with current best practices regarding the diagnosis and treatment of outliers, an additional difficulty arises concerning the mathematical implementation of the recommended methods. In this vignette, we provide an overview of current recommendations and best practices and demonstrate how they can easily and conveniently be implemented in the R statistical computing software, using the *{performance}* package of the *easystats* ecosystem. We cover univariate, multivariate, and model-based statistical outlier detection methods, their recommended threshold, standard output, and plotting methods. We conclude with recommendations on the handling of outliers: the different theoretical types of outliers, whether to exclude or winsorize them, and the importance of transparency.
 
 # Statement of Need