Added JOSS paper

JOSS/paper.bib

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+@ARTICLE{Bakdash2017,
+  title    = "Repeated Measures Correlation",
+  author   = "Bakdash, Jonathan Z and Marusich, Laura R",
+  abstract = "Repeated measures correlation (rmcorr) is a statistical technique
+              for determining the common within-individual association for
+              paired measures assessed on two or more occasions for multiple
+              individuals. Simple regression/correlation is often applied to
+              non-independent observations or aggregated data; this may produce
+              biased, specious results due to violation of independence and/or
+              differing patterns between-participants versus
+              within-participants. Unlike simple regression/correlation, rmcorr
+              does not violate the assumption of independence of observations.
+              Also, rmcorr tends to have much greater statistical power because
+              neither averaging nor aggregation is necessary for an
+              intra-individual research question. Rmcorr estimates the common
+              regression slope, the association shared among individuals. To
+              make rmcorr accessible, we provide background information for its
+              assumptions and equations, visualization, power, and tradeoffs
+              with rmcorr compared to multilevel modeling. We introduce the R
+              package (rmcorr) and demonstrate its use for inferential
+              statistics and visualization with two example datasets. The
+              examples are used to illustrate research questions at different
+              levels of analysis, intra-individual, and inter-individual.
+              Rmcorr is well-suited for research questions regarding the common
+              linear association in paired repeated measures data. All results
+              are fully reproducible.",
+  journal  = "Front. Psychol.",
+  volume   =  8,
+  pages    = "456",
+  month    =  apr,
+  year     =  2017,
+  keywords = "correlation; individual differences; intra-individual; multilevel
+              modeling; repeated measures; statistical power",
+  language = "en"
+}
+
+
+@ARTICLE{Pernet2012,
+  title     = "Robust correlation analyses: false positive and power validation
+               using a new open source matlab toolbox",
+  author    = "Pernet, Cyril R and Wilcox, Rand and Rousselet, Guillaume A",
+  abstract  = "Pearson's correlation measures the strength of the association
+               between two variables. The technique is, however, restricted to
+               linear associations and is overly sensitive to outliers. Indeed,
+               a single outlier can result in a highly inaccurate summary of
+               the data. Yet, it remains the most commonly used measure of
+               association in psychology research. Here we describe a free
+               Matlab((R)) based toolbox
+               (http://sourceforge.net/projects/robustcorrtool/) that computes
+               robust measures of association between two or more random
+               variables: the percentage-bend correlation and
+               skipped-correlations. After illustrating how to use the toolbox,
+               we show that robust methods, where outliers are down weighted or
+               removed and accounted for in significance testing, provide
+               better estimates of the true association with accurate false
+               positive control and without loss of power. The different
+               correlation methods were tested with normal data and normal data
+               contaminated with marginal or bivariate outliers. We report
+               estimates of effect size, false positive rate and power, and
+               advise on which technique to use depending on the data at hand.",
+  journal   = "Front. Psychol.",
+  publisher = "frontiersin.org",
+  volume    =  3,
+  pages     = "606",
+  year      =  2012,
+  keywords  = "MATLAB; correlation; outliers; power; robust statistics",
+  language  = "en"
+}
+
+
+@ARTICLE{Berens2009,
+  title    = "{CircStat}: A {MATLAB} Toolbox for Circular Statistics",
+  author   = "Berens, Philipp",
+  abstract = "Directional data is ubiquitious in science. Due to its circular
+              nature such data cannot be analyzed with commonly used
+              statistical techniques. Despite the rapid development of
+              specialized methods for directional statistics over the last
+              fifty years, there is only little software available that makes
+              such methods easy to use for practioners. Most importantly, one
+              of the most commonly used programming languages in biosciences,
+              MATLAB, is currently not supporting directional statistics. To
+              remedy this situation, we have implemented the CircStat toolbox
+              for MATLAB which provides methods for the descriptive and
+              inferential statistical analysis of directional data. We cover
+              the statistical background of the available methods and describe
+              how to apply them to data. Finally, we analyze a dataset from
+              neurophysiology to demonstrate the capabilities of the CircStat
+              toolbox.",
+  journal  = "Journal of Statistical Software, Articles",
+  volume   =  31,
+  number   =  10,
+  pages    = "1--21",
+  year     =  2009
+}
+
+@InProceedings{ Pandas,
+  author    = { Wes McKinney },
+  title     = { Data Structures for Statistical Computing in Python },
+  booktitle = { Proceedings of the 9th Python in Science Conference },
+  pages     = { 51 - 56 },
+  year      = { 2010 },
+  editor    = { Stefan van der Walt and Jarrod Millman }
+}

JOSS/paper.md

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+---
+title: 'Pingouin: statistics in Python'
+tags:
+  - statistics
+  - python
+  - data analysis
+  - pandas
+authors:
+ - name: Raphael Vallat
+   orcid: 0000-0003-1779-7653
+   affiliation: "1"
+affiliations:
+ - name: Department of Psychology, University of California, Berkeley.
+   index: 1
+date: 05 October 2018
+bibliography: paper.bib
+---
+
+# Summary
+
+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.
+
+# Citations
+
+Citations to entries in paper.bib should be in [rMarkdown](http://rmarkdown.rstudio.com/authoring_bibliographies_and_citations.html) format.
+
+# References