Pingouin

**New functions**

- Added [pingouin.plot_rm_corr()](https://pingouin-stats.org/generated/pingouin.plot_rm_corr.html#pingouin.plot_rm_corr) to plot a repeated measures correlation

**Enhancements**

- Added the `relimp` argument to [pingouin.linear_regression()](https://pingouin-stats.org/generated/pingouin.linear_regression.html#pingouin.linear_regression) to return the relative importance (= contribution) of each individual predictor to the R^2 of the full model.
- Complete refactoring of [pingouin.intraclass_corr()](https://pingouin-stats.org/generated/pingouin.intraclass_corr.html#pingouin.intraclass_corr) to closely match the R implementation in the [psych](https://cran.r-project.org/web/packages/psych/psych.pdf) package. Pingouin now returns the 6 types of ICC, together with F values, p-values, degrees of freedom and confidence intervals.
- The [pingouin.plot_shift()](https://pingouin-stats.org/generated/pingouin.plot_shift.html#pingouin.plot_shift) now 1) uses the Harrel-Davis robust quantile estimator in conjunction with a bias-corrected bootstrap confidence intervals, and 2) support paired samples.
- Added the axis argument to [pingouin.harrelldavis()](https://pingouin-stats.org/generated/pingouin.harrelldavis.html#pingouin.harrelldavis) to support 2D arrays.

Minor release with mostly internal code refactoring.

See full changelog at: https://pingouin-stats.org/changelog.html#v0-2-9-september-2019

See full changelog at: https://pingouin-stats.org/changelog.html#v0-2-8-july-2019

This is a minor release, mainly to fix dependency issues between scipy and statsmodels.

**Dependencies**

a. Pingouin now requires statsmodels>=0.10.0 (latest release June 2019) and is compatible with SciPy 1.3.0.

**Enhancements**

a. Added support for long-format dataframe in `pingouin.sphericity` and `pingouin.epsilon`.
b. Added support for two within-factors interaction in `pingouin.sphericity` and `pingouin.epsilon` (for the former, granted that at least one of them has no more than two levels.)

**New functions**

a. Added `pingouin.power_rm_anova` function.

**Bugfixes**

- Fixed **ERROR in two-sided p-value for Wilcoxon test** (`pingouin.wilcoxon()`), the p-values were accidentally squared, and therefore smaller. Make sure to always use the latest release of Pingouin.
- `pingouin.wilcoxon()` now uses the continuity correction by default (the documentation was saying that the correction was applied but it was not applied in the code.)
- The show_median argument of the `pingouin.plot_shift()` function was not working properly when the percentiles were different that the default parameters.

**Dependencies**

- The current release of statsmodels (0.9.0) is not compatible with the newest release of Scipy (1.3.0). In order to avoid compatibility issues in the `pingouin.ancova()` and `pingouin.anova()` functions (which rely on statsmodels for certain cases), Pingouin will require SciPy < 1.3.0 until a new stable version of statsmodels is released.

**New functions**

- Added `pingouin.chi2_independence()` tests.
- Added `pingouin.chi2_mcnemar()` tests.
- Added `pingouin.power_chi2()` function.
- Added `pingouin.bayesfactor_binom()` function.

**Enhancements**

- `pingouin.linear_regression()` now returns the residuals.
- Completely rewrote `pingouin.normality()` function, which now support pandas DataFrame (wide & long format), multiple normality tests (`scipy.stats.shapiro()`, `scipy.stats.normaltest()`), and an automatic casewise removal of missing values.
- Completely rewrote `pingouin.homoscedasticity()` function, which now support pandas DataFrame (wide & long format).
- Faster and more accurate algorithm in `pingouin.bayesfactor_pearson()` (same algorithm as JASP).
- Support for one-sided Bayes Factors in `pingouin.bayesfactor_pearson()`.
- Better handling of required parameters in `pingouin.qqplot()`.
- The epsilon value for the interaction term in `pingouin.rm_anova()` are now computed using the Greenhouse-Geisser method instead of the lower bound. A warning message has been added to the documentation to alert the user that the value might slightly differ than from R or JASP.

**Contributors**

- Raphael Vallat
- Arthur Paulino

Major release with several bugfixes, new functions, and many internal improvements:

**MAJOR BUG FIXES**

- Fixed error in p-values for one-sample one-sided T-test (pingouin.ttest()), the two-sided p-value was divided by 4 and not by 2, resulting in inaccurate (smaller) one-sided p-values.
- Fixed global error for unbalanced two-way ANOVA (pingouin.anova()), the sums of squares were wrong, and as a consequence so were the F and p-values. In case of unbalanced design, Pingouin now computes a type II sums of squares via a call to the statsmodels package.
- The epsilon factor for the interaction term in two-way repeated measures ANOVA (pingouin.rm_anova()) is now computed using the lower bound approach. This is more conservative than the Greenhouse-Geisser approach and therefore give (slightly) higher p-values. The reason for choosing this is that the Greenhouse-Geisser values for the interaction term differ than the ones returned by R and JASP. This will be hopefully fixed in future releases.

**New functions**

- Added pingouin.multivariate_ttest() (Hotelling T-squared) test.
- Added pingouin.cronbach_alpha() function.
- Added pingouin.plot_shift() function.
- Several functions of pandas can now be directly used as pandas.DataFrame methods.
- Added pingouin.pcorr() method to compute the partial Pearson correlation matrix of a pandas.DataFrame (similar to the pcor function in the ppcor package).
- The pingouin.partial_corr() now supports semi-partial correlation.

**Enhancements**

- The pingouin.rm_corr() function now returns a pandas.DataFrame with the r-value, degrees of freedom, p-value, confidence intervals and power.
- pingouin.compute_esci() now works for paired and one-sample Cohen d.
- pingouin.bayesfactor_ttest() and pingouin.bayesfactor_pearson() now return a formatted str and not a float.
- pingouin.pairwise_ttests() now returns the degrees of freedom (dof).
- Better rounding of float in pingouin.pairwise_ttests().
- Support for wide-format data in pingouin.rm_anova()
- pingouin.ttest() now returns the confidence intervals around the T-values.

**Missing values**

- pingouin.remove_na() and pingouin.remove_rm_na() are now external function documented in the API.
- pingouin.remove_rm_na() now works with multiple within-factors.
- pingouin.remove_na() now works with 2D arrays.
- Removed the remove_na argument in pingouin.rm_anova() and pingouin.mixed_anova(), an automatic listwise deletion of missing values is applied (same behavior as JASP). Note that this was also the default behavior of Pingouin, but the user could also specify not to remove the missing values, which most likely returned inaccurate results.
- The pingouin.ancova() function now applies an automatic listwise deletion of missing values.
- Added remove_na argument (default = False) in pingouin.linear_regression() and pingouin.logistic_regression() functions
- Missing values are automatically removed in the pingouin.anova() function.

**Contributors**

- Raphael Vallat
- Nicolas Legrand