Hi everyone,

I always wanted to learn Julia, and on the other hand, I never found a satisfying library to conduct statistical tests. I used Pingouin, a stats library in Python made by Raphael Vallat, and I always wished to have an equivalent package in Julia. So here is my version, completely coded in Julia, (pre-release with only a limited set of features).

As of now, Pingouin.jl 0.1.0 (GitHub - clementpoiret/Pingouin.jl: Reimplementation of Raphaelvallat's Pingouin in Julia) supports distribution-related functions such as:

- Anderson-Darling test of distribution,
- Geometric standard (Z) score,
- Levene & Bartlett tests for homoscedasticity,
- Shapiro-Wilk, Shapiro-Francia and Jarque Bera tests of normality,
- Mauchly and JNS tests for sphericity,
- Epsilon adjustement factor for repeated measures (e.g. i.e. Greenhouse-Geisser, Huynh-Feldt, Lower bound).

It also supports effect sizes-related functions:

Effect sizes between two Arrays:

- Unbiased Cohen d,
- Hedges g,
- Glass delta,
- correlation coefficient (pearson),
- Eta-square,
- Odds ratio,
- Area Under the Curve,
- Common Language Effect Size.

The conversion of pearsonās r and cohenās d to:

- Unbiased Cohen d,
- Hedges g,
- Eta-square,
- Odds ratio,
- Area Under the Curve.

But also the computation of effect sizes from T-values, parametric confidence intervals around a Cohen d or a correlation coefficient, and bootstrapped confidence intervals of univariate and bivariate functions.

The main goal is to provide a really a simple API, for simple and advanced statistics. The 0.1.0 will soon be published to the default julia package registry.

It is my first real project in Julia, so I really hope youāll like it. Iām a newbie, so feel free to give any suggestions, contributions. Feel free to make any remarks, or whatever you want, I want to improve my Julia skills

The next release will include paired and unpaired non-parametric tests such as Mann-Whitney U, Wilcoxon Signed Rank, or Friedman.