Pingouin

Major release with several new functions as well as many internal improvements.

**Correlation**

- Added pingouin.distance_corr() (distance correlation) function.
- pingouin.rm_corr() now requires at least 3 unique subjects (same behavior as the original R package).
- The pingouin.pairwise_corr() is faster and returns the number of outlier if a robust correlation is used.
- Added support for 2D level in the pingouin.pairwise_corr(). See Jupyter notebooks for examples.
- Added support for partial correlation in the pingouin.pairwise_corr() function.
- Greatly improved execution speed of pingouin.correlation.skipped() function.
- Added default random state to compute the Min Covariance Determinant in the pingouin.correlation.skipped() function.
- The default number of bootstrap samples for the pingouin.correlation.shepherd() function is now set to 200 (previously 2000) to increase computation speed.
- pingouin.partial_corr() now automatically drops rows with missing values.

**Datasets**

- Renamed pingouin.read_dataset() and pingouin.list_dataset() (before one needed to call these functions by calling pingouin.datasets)

**Pairwise T-tests and multi-comparisons**

- Added support for non-parametric pairwise tests in pingouin.pairwise_ttests() function.
- Common language effect size (CLES) is now reported by default in pingouin.pairwise_ttests() function.
- CLES is now implemented in the pingouin.compute_effsize() function.
- Better code, doc and testing for the functions in multicomp.py.
- P-values adjustment methods now do not take into account NaN values (same behavior as the R function p.adjust)

**Plotting**

- Added pingouin.plot_paired() function.

**Regression**

- NaN are now automatically removed in pingouin.mediation_analysis().
- The pingouin.linear_regression() and pingouin.logistic_regression() now fail if NaN / Inf are present in the target or predictors variables. The user must remove then before running these functions.
- Added support for multiple parallel mediator in pingouin.mediation_analysis().
- Added support for covariates in pingouin.mediation_analysis().
- Added seed argument to pingouin.mediation_analysis() for reproducible results.
- pingouin.mediation_analysis() now returns two-sided p-values computed with a permutation test.
- Added pingouin.utils._perm_pval() to compute p-value from a permutation test.

**Bugs and tests**

- Travis and AppVeyor test for Python 3.5, 3.6 and 3.7.
- Better doctest & improved examples for many functions.
- Fixed bug with pingouin.mad() when axis was not 0.

See full changelog at: https://pingouin-stats.org/changelog.html

See full changelog:
https://pingouin-stats.org/changelog.html#v0-2-2-december-2018

**MINOR**:
- JOSS paper citation
- Better confidence intervals

See full changelog:
https://raphaelvallat.github.io/pingouin/build/html/changelog.html#v0-2-1-november-2018

MAJOR changes in code and documentation, see full changelog here: https://raphaelvallat.github.io/pingouin/build/html/changelog.html#v0-2-0-november-2018

**Minor release:**

- Fixed dataset names in MANIFEST.in (.csv files were not copy-pasted with pip)
- Added circ_vtest function
- Added multivariate_normality function (Henze-Zirkler’s Multivariate Normality Test)
- Renamed functions test_normality, test_sphericity and test_homoscedasticity to normality, sphericity and homoscedasticity to avoid bugs with pytest.
- Moved distribution tests from parametric.py to distribution.py