added rouder 2009 reference

JOSS/paper.bib

@@ -1,3 +1,31 @@
+@ARTICLE{Rouder2009,
+  title     = "Bayesian t tests for accepting and rejecting the null hypothesis",
+  author    = "Rouder, Jeffrey N and Speckman, Paul L and Sun, Dongchu and
+               Morey, Richard D and Iverson, Geoffrey",
+  abstract  = "Progress in science often comes from discovering invariances in
+               relationships among variables; these invariances often
+               correspond to null hypotheses. As is commonly known, it is not
+               possible to state evidence for the null hypothesis in
+               conventional significance testing. Here we highlight a Bayes
+               factor alternative to the conventional t test that will allow
+               researchers to express preference for either the null hypothesis
+               or the alternative. The Bayes factor has a natural and
+               straightforward interpretation, is based on reasonable
+               assumptions, and has better properties than other methods of
+               inference that have been advocated in the psychological
+               literature. To facilitate use of the Bayes factor, we provide an
+               easy-to-use, Web-based program that performs the necessary
+               calculations.",
+  journal   = "Psychon. Bull. Rev.",
+  publisher = "Springer",
+  volume    =  16,
+  number    =  2,
+  pages     = "225--237",
+  month     =  apr,
+  year      =  2009,
+  language  = "en"
+}
+
 @ARTICLE{Bakdash2017,
   title    = "Repeated Measures Correlation",
   author   = "Bakdash, Jonathan Z and Marusich, Laura R",

JOSS/paper.md

@@ -20,7 +20,7 @@ bibliography: paper.bib
 
 Python is currently the fastest growing programming language in the world, thanks to its ease-of-use, fast learning curve and its numerous high quality packages for data science and machine-learning. Surprisingly however, Python is far behind the R programming language when it comes to general statistics and for this reason many scientists still rely heavily on R to perform their statistical analyses.
 
-In this paper, we present ``Pingouin``, an open-source Python package aimed at partially filling this gap by providing easy-to-use functions for computing some of the main statistical tests that scientists use on an every day basis. This includes basics functions such as ANOVAs, ANCOVAs, post-hoc tests, non-parametric tests, effect sizes, as well as more advanced functions such as Bayesian T-tests, repeated measures correlations [@Bakdash2017], robust correlations [@Pernet2012] and circular statistics [@Berens2009], to cite but a few. ``Pingouin`` is written in Python 3 and is mostly built on top of the Pandas [@Pandas] library, therefore allowing a fluid integration within a data analysis pipeline. ``Pingouin`` comes with an extensive documentation and API as well as with several Jupyter notebook examples.
+In this paper, we present ``Pingouin``, an open-source Python package aimed at partially filling this gap by providing easy-to-use functions for computing some of the main statistical tests that scientists use on an every day basis. This includes basics functions such as ANOVAs, ANCOVAs, post-hoc tests, non-parametric tests, effect sizes, as well as more advanced functions such as Bayesian T-tests [@Rouder2009], repeated measures correlations [@Bakdash2017], robust correlations [@Pernet2012] and circular statistics [@Berens2009], to cite but a few. ``Pingouin`` is written in Python 3 and is mostly built on top of the Pandas [@Pandas] library, therefore allowing a fluid integration within a data analysis pipeline. ``Pingouin`` comes with an extensive documentation and API as well as with several Jupyter notebook examples.
 
 # Citations