fixes #619

papers/JOSE/paper.Rmd

@@ -111,10 +111,11 @@ Beyond the challenge of keeping up-to-date with current best practices regarding
 
 Real-life data often contain observations that can be considered *abnormal* when compared to the main population. The cause of it---be it because they belong to a different distribution (originating from a different generative process) or simply being extreme cases, statistically rare but not impossible---can be hard to assess, and the boundaries of "abnormal" difficult to define.
 
-Nonetheless, the improper handling of these outliers can substantially affect statistical model estimations, biasing effect estimations and weakening the models' predictive performance.
-It is thus essential to address this problem in a thoughtful manner. Yet, despite the existence of established recommendations and guidelines, many researchers still do not treat outliers in a consistent manner, or do so using inappropriate strategies [@simmons2011false; @leys2013outliers].
+Nonetheless, the improper handling of these outliers can substantially affect statistical model estimations, biasing effect estimations and weakening the models' predictive performance. It is thus essential to address this problem in a thoughtful manner. Yet, despite the existence of established recommendations and guidelines, many researchers still do not treat outliers in a consistent manner, or do so using inappropriate strategies [@simmons2011false; @leys2013outliers].
 
-One possible reason is that researchers are not aware of the existing recommendations, or do not know how to implement them using their analysis software. In this paper, we show how to follow current best practices for automatic and reproducible statistical outlier detection (SOD) using R and the *{performance}* package [@ludecke2021performance], which is part of the *easystats* ecosystem of packages that build an R framework for easy statistical modeling, visualization, and reporting [@easystatspackage].
+One possible reason is that researchers are not aware of the existing recommendations, or do not know how to implement them using their analysis software. In this paper, we show how to follow current best practices for automatic and reproducible statistical outlier detection (SOD) using R and the *{performance}* package [@ludecke2021performance], which is part of the *easystats* ecosystem of packages that build an R framework for easy statistical modeling, visualization, and reporting [@easystatspackage]. Installation instructions can be found on [GitHub](https://github.com/easystats/performance) or its [website](https://easystats.github.io/performance/), and its list of dependencies on [CRAN](https://cran.r-project.org/package=performance).
+
+The instructional materials that follow is aimed at an audience of researchers who want to follow good practices, and is appropriate for advanced undergraduate students, graduate students, professors, or professionals having to deal with the nuances of outlier treatment.
 
 # Identifying Outliers