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What's new

v0.5.5 (September 2024)

This is a minor release with several bugfixes, and major updates to the internal structure and sphinx documentation.

See the full changelog for 0.5.5.

v0.5.4 (January 2024)

This is a minor release with several bugfixes and no new features. The new version is tested for Python 3.8-3.11 (but should also work with Python 3.12). See the full changelog for 0.5.4.

This release requires pandas≥1.5. We recommend scipy≥1.11.0.

v0.5.3 (December 2022)

Bugfixes

  • Fixed a bug where the boolean value returned by :py:func:`pingouin.anderson` was inverted. It returned True when the data was NOT coming from the tested distribution, and vice versa. PR 308.
  • Fixed misleading documentation and input_type in the :py:func:`pingouin.convert_effsize` function. When converting from a Cohen's d effect size to a correlation coefficient, the resulting correlation is not a Pearson correlation but instead a point-biserial correlation. To avoid any confusion, input_type='r' has been deprecated and replaced with input_type='pointbiserialr'. For more details, see issue 302.

New function

We have added the :py:func:`pingouin.ptests` function to calculate a T-test (T- and p-values) between all pairs of columns in a given dataframe. This is the T-test equivalent of :py:func:`pingouin.rcorr`. It can only be used as a :py:class:`pandas.DataFrame` method, not as a standalone function. The output is a square dataframe with the T-values on the lower triangle and the p-values on the upper triangle.

>>> import pingouin as pg
>>> df = pg.read_dataset('pairwise_corr').iloc[:30, 1:]
>>> df.columns = ["N", "E", "O", "A", "C"]
>>> df.ptests()
        N       E      O      A    C
N       -     ***    ***    ***  ***
E  -8.397       -                ***
O  -8.585  -0.483      -         ***
A  -9.026   0.278  0.786      -  ***
C  -4.759   3.753  4.128  3.802    -

Improvements

v0.5.2 (June 2022)

Bugfixes

  1. The eta-squared (n2) effect size was not properly calculated in one-way and two-way repeated measures ANOVAs. Specifically, Pingouin followed the same behavior as JASP, i.e. the eta-squared was the same as the partial eta-squared. However, as explained in issue 251, this behavior is not valid. In one-way ANOVA design, the eta-squared should be equal to the generalized eta-squared. Note that, as of March 2022, this bug is also present in JASP. We have therefore updated the unit tests to use JAMOVI instead.

Warning

Please double check any effect sizes previously obtained with the :py:func:`pingouin.rm_anova` function.

  1. Fixed invalid resampling behavior for bivariate functions in :py:func:`pingouin.compute_bootci` when x and y were not paired. PR 281.
  2. Fixed bug where confidence (previously ci) was ignored when calculating the bootstrapped confidence intervals in :py:func:`pingouin.plot_shift`. PR 282.

Enhancements

  1. The :py:func:`pingouin.pairwise_ttests` has been renamed to :py:func:`pingouin.pairwise_tests`. Non-parametric tests are also supported in this function with the parametric=False argument, and thus the name "ttests" was misleading (see issue 209).
  2. Allow :py:func:`pingouin.bayesfactor_binom` to take Beta alternative model. PR 252.
  3. Allow keyword arguments for logistic regression in :py:func:`pingouin.mediation_analysis`. PR 245.
  4. Speed improvements for the Holm and FDR correction in :py:func:`pingouin.multicomp`. PR 271.
  5. Speed improvements univariate functions in :py:func:`pingouin.compute_bootci` (e.g. func="mean" is now vectorized).
  6. Rename eta to eta_squared in :py:func:`pingouin.power_anova` and :py:func:`pingouin.power_rm_anova` to avoid any confusion. PR 280.
  7. Use black code formatting.
  8. Add support for DataMatrix objects. PR 286.

Dependencies

  1. Force scikit-learn<1.1.0 to avoid bug in :py:func:`pingouin.logistic_regression`. PR 272.

v0.5.1 (February 2022)

This is a minor release, with several bugfixes and improvements. This release is compatible with SciPy 1.8 and Pandas 1.4.

Bugfixes

  1. Added support for SciPy 1.8 and Pandas 1.4. PR 234.
  2. Fixed bug where :py:func:`pingouin.rm_anova` and :py:func:`pingouin.mixed_anova` changed the dtypes of categorical columns in-place (issue 224).

Enhancements

  1. Faster implementation of :py:func:`pingouin.gzscore`, adding all options available in zscore: axis, ddof and nan_policy. Warning: this functions is deprecated and will be removed in pingouin 0.7.0 (use :py:func:`scipy.stats.gzscore` instead). PR 210.
  2. Replace use of statsmodels' studentized range distribution functions with more SciPy's more accurate :py:func:`scipy.stats.studentized_range`. PR 229.
  3. Add support for optional keywords argument in the :py:func:`pingouin.homoscedasticity` function (issue 218).
  4. Add support for the Jarque-Bera test in :py:func:`pingouin.normality` (issue 216).

Lastly, we have also deprecated the Gitter forum in favor of GitHub Discussions. Please use Discussions to ask questions, share ideas / tips and engage with the Pingouin community!

v0.5.0 (October 2021)

This is a MAJOR RELEASE with several important bugfixes. We recommend all users to upgrade to this new version.

BUGFIX - Repeated measurements

This release fixes several critical issues related to how Pingouin handles missing values in repeated measurements. The following functions have been corrected:

A full description of the issue, with code and example, can be found at: #206. In short, in Pingouin <0.5.0, listwise deletion of subjects (or rows) with missing values was not strictly enforced in repeated measures or mixed ANOVA, depending on the input data format (if missing values were explicit or implicit). Pingouin 0.5.0 now uses a stricter complete-case analysis regardless of the input data format, which is the same behavior as JASP.

Furthermore, the :py:func:`pingouin.remove_rm_na` has been deprecated. Instead, listwise deletion of rows with missing values in repeated measurements is now performed using:

>>> data_piv = data.pivot_table(index=subject, columns=within, values=dv)
>>> data_piv = data_piv.dropna()  # Listwise deletion
>>> data = data_piv.melt(ignore_index=False, value_name=dv).reset_index()

BUGFIX - Strict listwise deletion in pairwise_ttests when repeated measures are present

This is related to the previous issue. In mixed design, listwise deletion (complete-case analysis) was not strictly enforced in :py:func:`pingouin.pairwise_ttests` for the between-subject and interaction T-tests. In other words, the between-subject and interaction T-tests were calculated using a pairwise-deletion approach, even with nan_policy="pairwise". The same issue occured in two-way repeated measures design, in which no strict listwise deletion was performed prior to calculating the T-tests, even with nan_policy="pairwise".

This has now been fixed such that Pingouin will always perform a strict listwise deletion whenever repeated measurements are present when nan_policy="listwise" (default). This complete-case analysis behavior can be disabled with nan_policy="pairwise", in which case missing values will be removed separately for each contrast. This may not be appropriate for post-hoc analysis following a repeated measures or mixed ANOVA, which is always conducted on complete-case data.

BUGFIX - Homoscedasticity

The :py:func:`pingouin.homoscedasticity` gave WRONG results for wide-format dataframes because the test was incorrectly calculated on the transposed data. See issue 204.

Enhancements

  1. Partial correlation functions (:py:func:`pingouin.pcorr` and :py:func:`pingouin.partial_corr`) now use :py:func:`numpy.linalg.pinv` with hermitian=True, which improves numerical stability. See issue 198.
  2. Added support for integer column names in most functions. Previously, Pingouin raised an error if the column names were integers. See issue 201.
  3. :py:func:`pingouin.pairwise_corr` now works when the column names of the dataframe are integer, and better support numpy.arrays in the columns argument.
  4. Added support for wide-format dataframe in :py:func:`pingouin.friedman` and :py:func:`pingouin.cochran`

v0.4.0 (August 2021)

Major upgrade of the dependencies. This release requires Python 3.7+, SciPy 1.7+, NumPy 1.19+ and Pandas 1.0+. Pingouin uses the alternative argument that has been added to several statistical functions of Scipy 1.7+ (see below). However, SciPy 1.7+ requires Python 3.7+. We recommend all users to upgrade to the latest version of Pingouin.

Major enhancements

Directional testing

The tail argument has been renamed to alternative in all Pingouin functions to be consistent with SciPy and R (#185). Furthermore, "alternative='one-sided'" has now been deprecated. Instead, alternative must be one of "two-sided" (default), "greater" or "less". Again, this is the same behavior as SciPy and R.

Added support for directional testing with "alternative='greater'" and "alternative='less'" in :py:func:`pingouin.corr` (#176). As a result, the p-value, confidence intervals and power of the correlation will change depending on the directionality of the test. Support for directional testing has also been added to :py:func:`pingouin.power_corr` and :py:func:`pingouin.compute_esci`.

Finally, the tail argument has been removed from :py:func:`pingouin.rm_corr`, :py:func:`pingouin.circ_corrcc` and :py:func:`pingouin.circ_corrcl` to be consistent with the original R / Matlab implementations.

Partial correlation

Major refactoring of :py:func:`pingouin.partial_corr`, which now uses the same method as the R ppcor package, i.e. based on the inverse covariance matrix rather than the residuals of a linear regression. This new approach is faster and works better in some cases (such as Spearman partial correlation with binary variables, see issue 147). One caveat is that only the Pearson and Spearman correlation methods are now supported in partial/semi-partial correlation.

Box M test

Added the :py:func:`pingouin.box_m` function to calculate Box's M test for equality of covariance matrices (#175).

Minor enhancements

v0.3.12 (May 2021)

Bugfixes

This release fixes a critical error in :py:func:`pingouin.partial_corr`: the number of covariates was not taken into account when calculating the degrees of freedom of the partial correlation, thus leading to incorrect results (except for the correlation coefficient which remained unaffected). For more details, please see issue 171.

In addition to fixing the p-values and 95% confidence intervals, the statistical power and Bayes Factor have been removed from the output of :py:func:`pingouin.partial_corr`, at least temporary until we can make sure that these give exact results.

We have also fixed a minor bug in the robust skipped and shepherd correlation (see :py:func:`pingouin.corr`), for which the calculation of the confidence intervals and statistical power did not take into account the number of outliers. These are now calculated only on the cleaned data.

Warning

We therefore strongly recommend that all users UPDATE Pingouin (pip install -U pingouin) and CHECK ANY RESULTS obtained with the :py:func:`pingouin.partial_corr` function.

Enhancements

  1. Major refactoring of :py:func:`pingouin.plot_blandaltman`, which now has many additional parameters. It also uses a T distribution instead of a normal distribution to estimate the 95% confidence intervals of the mean difference and agreement limits. See issue 167.
  2. For clarity, the z, r2 and adj_r2 have been removed from the output of :py:func:`pingouin.corr` and :py:func:`pingouin.pairwise_corr`, as these can be readily calculated from the correlation coefficient.
  3. Better testing against R for :py:func:`pingouin.partial_corr` and :py:func:`pingouin.corr`.

v0.3.11 (April 2021)

Bugfixes

  1. Fix invalid computation of the robust skipped correlation in :py:func:`pingouin.corr` (see issue 164).
  2. Passing a wrong tail argument to :py:func:`pingouin.corr` now always raises an error (see PR 160). In previous versions of pingouin, using any method other than "pearson" and a wrong tail argument such as "two-tailed" or "both" (instead of the correct "two-sided") may have resulted in silently returning a one-sided p-value.
  3. Reverted changes made in :py:func:`pingouin.pairwise_corr` which led to Pingouin calculating the correlations between the DV columns and the covariates, thus artificially increasing the number of pairwise comparisons (see issue 162).

v0.3.10 (February 2021)

Bugfix

This release fixes an error in the calculation of the p-values in the :py:func:`pingouin.pairwise_tukey` and :py:func:`pingouin.pairwise_gameshowell` functions (see PR156). Old versions of Pingouin used an incorrect algorithm for the studentized range approximation, which resulted in (slightly) incorrect p-values. In most cases, the error did not seem to affect the significance of the p-values. The new version of Pingouin now uses statsmodels internal implementation of the Gleason (1999) algorithm to estimate the p-values.

Please note that the Pingouin p-values may be slightly different than R (and JASP), because it uses a different algorithm. However, this does not seem to affect the significance levels of the p-values (i.e. a p-value below 0.05 in JASP is likely to be below 0.05 in Pingouin, and vice versa).

We therefore recommend that all users UPDATE Pingouin (pip install -U pingouin) and CHECK ANY RESULTS obtained with the :py:func:`pingouin.pairwise_tukey` and :py:func:`pingouin.pairwise_gameshowell` functions.

v0.3.9 (January 2021)

Bugfix

This release fixes a CRITICAL ERROR in the :py:func:`pingouin.pairwise_ttests` function (see issue 151). The bug concerns one-way and two-way repeated measures pairwise T-tests. Until now, Pingouin implicitly assumed that the dataframe was sorted such that the ordering of the subject was the same across all repeated measurements (e.g. the third values in the repeated measurements always belonged to the same subject). This led to incorrect results when the dataframe was not sorted in such a way.

We therefore strongly recommend that all users UPDATE Pingouin (pip install -U pingouin) and CHECK ANY RESULTS obtained with the :py:func:`pingouin.pairwise_ttests` function. Note that the bug does not concern non-repeated measures pairwise T-test, since the ordering of the values does not matter in this case.

Furthermore, and to prevent a similar issue, we have now disabled marginal=False in two-way repeated measure design. As of this release, marginal=False will therefore only have an impact on the between-factor T-test(s) of a mixed design.

Deprecation

a. Removed the Glass delta effect size. Until now, Pingouin invalidly assumed that the control group was always the one with the lowest standard deviation. Since this cannot be verified, and to avoid any confusion, the Glass delta effect size has been completely removed from Pingouin. See issue 139.

Enhancements

  1. :py:func:`pingouin.plot_paired` now supports an arbitrary number of within-levels as well as horizontal plotting. See PR 133.
  2. :py:func:`pingouin.linear_regression` now handles a rank deficient design matrix X by producing a warning and trying to calculate the sum of squared residuals without relying on :py:func:`np.linalg.lstsq`. See issue 130.
  3. :py:func:`pingouin.friedman` now has an option to choose between Chi square test or F test method.
  4. Several minor improvements to the documentation and GitHub Actions. See PR150.
  5. Added support for kwargs in :py:func:`pingouin.corr` (see issue 138).
  6. Added confidence argument in :py:func:`pingouin.ttest` to allow for custom CI (see issue 152).

v0.3.8 (September 2020)

Bugfixes

  1. Fix a bug in in :py:func:`pingouin.ttest` in which the confidence intervals for one-sample T-test with y != 0 were invalid (e.g. pg.ttest(x=[4, 6, 7, 4], y=4)). See issue 119.

New features

  1. Added a pingouin.options module which can be used to set default options. For example, one can set the default decimal rounding of the output dataframe, either for the entire dataframe, per column, per row, or per cell. See PR120. For more details, please refer to notebooks/06_others.ipynb.

    import pingouin as pg
pg.options['round'] = None  # Default: no rounding
pg.options['round'] = 4
pg.options['round.column.CI95%'] = 2
pg.options['round.row.T-test'] = 2
pg.options['round.cell.[T-test]x[CI95%]'] = 2

Enhancements

  1. :py:func:`pingouin.linear_regression` now returns the processed X and y variables (Xw and yw for WLS) and the predicted values if as_dataframe=False. See issue 112.
  2. The Common Language Effect Size (CLES) in :py:func:`pingouin.mwu` is now calculated using the formula given by Vargha and Delaney 2000, which works better when ties are present in data. This is consistent with the :py:func:`pingouin.wilcoxon` and :py:func:`pingouin.compute_effsize` functions. See issue 114.
  3. Better handling of kwargs arguments in :py:func:`pingouin.plot_paired` (see PR 116).
  4. Added boxplot_in_front argument to the :py:func:`pingouin.plot_paired`. When set to True, the boxplot is displayed in front of the lines with a slight transparency. This can make the overall plot more readable when plotting data from a large number of subjects. (see PR 117).
  5. Better handling of Categorical columns in several functions (e.g. ANOVA). See issue 122.
  6. :py:func:`multivariate_normality` now also returns the test statistic. This function also comes with better unit testing against the MVN R package.
  7. :py:func:`pingouin.pairwise_corr` can now control for all covariates by excluding each specific set of column-combinations from the covariates to use for this combination, similar to :py:func:`pingouin.pcorr`. See PR 124.
  8. Bayes factor formatting is now handled via the options module. The default behaviour is unchanged (return as formatted string), but can easily be disabled by setting pingouin.options["round.column.BF10"] = None. See PR 126.

v0.3.7 (July 2020)

Bugfixes

This hotfix release brings important changes to the :py:func:`pingouin.pairwise_tukey` and :py:func:`pingouin.pairwise_gameshowell` functions. These two functions had been implemented soon after Pingouin's first release and were not as tested as more recent and widely-used functions. These two functions are now validated against JASP.

We strongly recommend that all users upgrade their version of Pingouin (pip install -U pingouin).

  1. Fixed a bug in :py:func:`pingouin.pairwise_tukey` and :py:func:`pingouin.pairwise_gameshowell` in which the group labels (columns A and B) were incorrect when the between column was encoded as a :py:class:`pandas.Categorical` with non-alphabetical categories order. This was caused by a discrepancy in how Numpy and Pandas sorted the categories in the between column. For more details, please refer to issue 111.
  2. Fixed a bug in :py:func:`pingouin.pairwise_gameshowell` in which the reported standard errors were slightly incorrect because of a typo in the code. However, the T-values and p-values were fortunately calculated using the correct standard errors, so this bug only impacted the values in the se column.
  3. Removed the tail and alpha argument from the in :py:func:`pingouin.pairwise_tukey` and :py:func:`pingouin.pairwise_gameshowell` functions to be consistent with JASP. Note that the alpha parameter did not have any impact. One-sided p-values were obtained by halving the two-sided p-values.

Error

Please check all previous code and results that called the :py:func:`pingouin.pairwise_tukey` or :py:func:`pingouin.pairwise_gameshowell` functions, especially if the between column was encoded as a :py:class:`pandas.Categorical`.

Deprecation

  1. We have now removed the :py:func:`pingouin.plot_skipped_corr` function, as we felt that it may not be useful or relevant to many users (see issue 105).

v0.3.6 (July 2020)

Bugfixes

  1. Changed the default scikit-learn solver in :py:func:`pingouin.logistic_regression` from 'lbfgs' to 'newton-cg' in order to get results that are always consistent with R or statsmodels. Previous version of Pingouin were based on the 'lbfgs' solver which internally applied a regularization of the intercept that may have led to different coefficients and p-values for the predictors of interest based on the scaling of these predictors (e.g very small or very large values). The new 'newton-cg' solver is scaling-independent, i.e. no regularization is applied to the intercept and p-values are therefore unchanged with different scaling of the data. If you prefer to keep the old behavior, just use: pingouin.logistic_regression(..., solver='lbfgs').
  2. Fixed invalid results in :py:func:`pingouin.logistic_regression` when fit_intercept=False was passed as a keyword argument to scikit-learn. The standard errors and p-values were still calculated by taking into account an intercept in the model.

Warning

We highly recommend double-checking all previous code and results that called the :py:func:`pingouin.logistic_regression` function, especially if it involved non-standardized predictors and/or custom keywords arguments passed to scikit-learn.

Enhancements

  1. Added within_first boolean argument to :py:func:`pingouin.pairwise_ttests`. This is useful in mixed design when one want to change the order of the interaction. The default behavior of Pingouin is to return the within * between pairwise tests for the interaction. Using within_first=False, one can now return the between * within pairwise tests. For more details, see issue 102 on GitHub.
  2. :py:func:`pingouin.list_dataset` now returns a dataframe instead of simply printing the output.
  3. Added the Palmer Station LTER Penguin dataset, which describes the flipper length and body mass for different species of penguins. It can be loaded with pingouin.read_dataset('penguins').
  4. Added the Tips dataset. It can be loaded with pingouin.read_dataset('tips').

v0.3.5 (June 2020)

Enhancements

  1. Added support for weighted linear regression in :py:func:`pingouin.linear_regression`. Users can now pass sample weights using the weights argument (similar to lm(..., weights) in R and LinearRegression.fit(X, y, sample_weight) in scikit-learn).

  2. The R^2 in :py:func:`pingouin.linear_regression` is now calculated in a similar manner as statsmodels and R, which give different results as :py:func:`sklearn.metrics.r2_score` when, and only when, no constant term (= intercept) is present in the predictor matrix. In that case, scikit-learn (and previous versions of Pingouin) uses the standard R^2 formula, which assumes a reference model that only includes an intercept:

    R^2 = 1 - \frac{\sum_i (y_i - \hat y_i)^2}{\sum_i (y_i - \bar y)^2}

    However, statsmodels, R, and newer versions of Pingouin use a modified formula, which uses a reference model corresponding to noise only (i.e. no intercept, as explained in this post):

    R_0^2 = 1 - \frac{\sum_i (y_i - \hat y_i)^2}{\sum_i y_i^2}

    Note that this only affects the (rare) cases when no intercept is present in the predictor matrix. Remember that Pingouin automatically add a constant term in :py:func:`pingouin.linear_regression`, a behavior that can be disabled using add_intercept=False.

  3. Added support for robust biweight midcorrelation ('bicor') in :py:func:`pingouin.corr` and :py:func:`pingouin.pairwise_corr`.

  4. The Common Language Effect Size (CLES) is now calculated using the formula given by Vargha and Delaney 2000, which works better when ties are present in data.

    \text{CL} = P(X > Y) + .5 \times P(X = Y)

    This applies to the :py:func:`pingouin.wilcoxon` and :py:func:`pingouin.compute_effsize` functions. Furthermore, the CLES is now tail-sensitive in the former, but not in the latter since tail is not a valid argument. In :py:func:`pingouin.compute_effsize`, the CLES thus always corresponds to the proportion of pairs where x is higher than y. For more details, please refer to PR #94.

  5. Confidence intervals around a Cohen d effect size are now calculated using a central T distribution instead of a standard normal distribution in the :py:func:`pingouin.compute_esci` function. This is consistent with the effsize R package.

Code

  1. Added support for unsigned integers in dtypes safety checks (see issue #93).

v0.3.4 (May 2020)

Bugfixes

  1. The Cohen d_{avg} for paired samples was previously calculated using eq. 10 in Lakens 2013. However, this equation was slightly different from the original proposed by Cumming 2012, and Lakens has since updated the equation in his effect size conversion spreadsheet. Pingouin now uses the correct formula, which is d_{avg} = \frac{\overline{X} - \overline{Y}}{\sqrt{\frac{(\sigma_1^2 + \sigma_2^2)}{2}}}.
  2. Fixed minor bug in internal function pingouin.utils._flatten_list that could lead to TypeError in :py:func:`pingouin.pairwise_ttests` with within/between factors encoded as integers (see issue #91).

New functions

  1. Added :py:func:`pingouin.convert_angles` function to convert circular data in arbitrary units to radians ([-\pi, \pi) range).

Enhancements

  1. Better documentation and testing for descriptive circular statistics functions.
  2. Added safety checks that angles is expressed in radians in circular statistics function.
  3. :py:func:`pingouin.circ_mean` and :py:func:`pingouin.circ_r` now perform calculations omitting missing values.
  4. Pingouin no longer changes the default matplotlib style to a Seaborn-default (see issue #85).
  5. Disabled rounding of float in most Pingouin functions in order to reduce numerical imprecision. For more details, please refer to issue #87. Users can still round the output using the :py:meth:`pandas.DataFrame.round` method, or changing the default precision of Pandas DataFrame with pandas.set_option.
  6. Disabled filling of missing values by '-' in some ANOVAs functions, which may have lead to dtypes issues.
  7. Added partial eta-squared (np2 column) to the output of :py:func:`pingouin.ancova` and :py:func:`pingouin.welch_anova`.
  8. Added the effsize option to :py:func:`pingouin.anova` and :py:func:`pingouin.ancova` to return different effect sizes. Must be one of 'np2' (partial eta-squared, default) or 'n2' (eta-squared).
  9. Added the effsize option to :py:func:`pingouin.rm_anova` and :py:func:`pingouin.mixed_anova` to return different effect sizes. Must be one of 'np2' (partial eta-squared, default), 'n2' (eta-squared) or ng2 (generalized eta-squared).

Code and dependencies

  1. Compatibility with Python 3.9 (see PR by tirkarthi).
  2. To avoid any confusion, the alpha argument has been renamed to angles in all circular statistics functions.
  3. Updated flake8 guidelines and added continuous integration for Python 3.8.
  4. Added the tabulate package as dependency. The tabulate package is used by the :py:func:`pingouin.print_table` function as well as the :py:meth:`pandas.DataFrame.to_markdown` function.

v0.3.3 (February 2020)

Bugfixes

  1. Fixed a bug in :py:func:`pingouin.pairwise_corr` caused by the deprecation of pandas.core.index in the new version of Pandas (1.0). For now, both Pandas 0.25 and Pandas 1.0 are supported.
  2. The standard deviation in :py:func:`pingouin.pairwise_ttests` when using return_desc=True is now calculated with np.nanstd(ddof=1) to be consistent with Pingouin/Pandas default unbiased standard deviation.

New functions

  1. Added :py:func:`pingouin.plot_circmean` function to plot the circular mean and circular vector length of a set of angles (in radians) on the unit circle.

v0.3.2 (January 2020)

Hotfix release to fix a critical issue with :py:func:`pingouin.pairwise_ttests` (see below). We strongly recommend that you update to the newest version of Pingouin and double-check your previous results if you've ever used the pairwise T-tests with more than one factor (e.g. mixed, factorial or 2-way repeated measures design).

Bugfixes

  1. MAJOR: Fixed a bug in :py:func:`pingouin.pairwise_ttests` when using mixed or two-way repeated measures design. Specifically, the T-tests were performed without averaging over repeated measurements first (i.e. without calculating the marginal means). Note that for mixed design, this only impacts the between-subject T-test(s). Practically speaking, this led to higher degrees of freedom (because they were conflated with the number of repeated measurements) and ultimately incorrect T and p-values because the assumption of independence was violated. Pingouin now averages over repeated measurements in mixed and two-way repeated measures design, which is the same behavior as JASP or JAMOVI. As a consequence, and when the data has only two groups, the between-subject p-value of the pairwise T-test should be (almost) equal to the p-value of the same factor in the :py:func:`pingouin.mixed_anova` function. The old behavior of Pingouin can still be obtained using the marginal=False argument.
  2. Minor: Added a check in :py:func:`pingouin.mixed_anova` to ensure that the subject variable has a unique set of values for each between-subject group defined in the between variable. For instance, the subject IDs for group1 are [1, 2, 3, 4, 5] and for group2 [6, 7, 8, 9, 10]. The function will throw an error if there are one or more overlapping subject IDs between groups (e.g. the subject IDs for group1 AND group2 are both [1, 2, 3, 4, 5]).
  3. Minor: Fixed a bug which caused the :py:func:`pingouin.plot_rm_corr` and :py:func:`pingouin.ancova` (with >1 covariates) to throw an error if any of the input variables started with a number (because of statsmodels / Patsy formula formatting).

Enhancements

  1. Upon loading, Pingouin will now use the outdated package to check and warn the user if a newer stable version is available.
  2. Globally removed the export_filename parameter, which allowed to export the output table to a .csv file. This helps simplify the API and testing. As an alternative, one can simply use pandas.to_csv() to export the output dataframe generated by Pingouin.
  3. Added the correction argument to :py:func:`pingouin.pairwise_ttests` to enable or disable Welch's correction for independent T-tests.

v0.3.1 (December 2019)

Bugfixes

  1. Fixed a bug in which missing values were removed from all columns in the dataframe in :py:func:`pingouin.kruskal`, even columns that were unrelated. See #74.
  2. The :py:func:`pingouin.power_corr` function now throws a warning and return a np.nan when the sample size is too low (and not an error like in previous version). This is to improve compatibility with the :py:func:`pingouin.pairwise_corr` function.
  3. Fixed quantile direction in the :py:func:`pingouin.plot_shift` function. In v0.3.0, the quantile subplot was incorrectly labelled as Y - X, but it was in fact calculating X - Y. See #73

v0.3.0 (November 2019)

New functions

  1. Added :py:func:`pingouin.plot_rm_corr` to plot a repeated measures correlation

Enhancements

  1. Added the relimp argument to :py:func:`pingouin.linear_regression` to return the relative importance (= contribution) of each individual predictor to the R^2 of the full model.
  2. Complete refactoring of :py:func:`pingouin.intraclass_corr` to closely match the R implementation in the psych package. Pingouin now returns the 6 types of ICC, together with F values, p-values, degrees of freedom and confidence intervals.
  3. The :py:func:`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.
  4. Added the axis argument to :py:func:`pingouin.harrelldavis` to support 2D arrays.

Older versions

.. dropdown:: **v0.2.9 (September 2019)**

   **Bugfixes**

   a. Disabled default l2 regularization of coefficients in :py:func:`pingouin.logistic_regression`. As pointed out by Eshin Jolly in `PR54 <https://github.com/raphaelvallat/pingouin/pull/54>`_, scikit-learn automatically applies a penalization of coefficients, which in turn makes the estimation of standard errors and p-values not totally correct/interpretable. This regularization behavior is now disabled, resulting in the same behavior as R ``glm(..., family=binomial)``.

   **Code and dependencies**

   a. Pandas methods are now internally defined using the `pandas_flavor package <https://github.com/Zsailer/pandas_flavor>`_ package.
   b. Internal code refactoring of the :py:func:`pingouin.pairwise_ttests` (to slightly speed up computation and improve memory usage).
   c. The first argument of the :py:func:`pingouin.anova`, :py:func:`pingouin.ancova`, :py:func:`pingouin.welch_anova`, :py:func:`pingouin.pairwise_ttests`, :py:func:`pingouin.pairwise_tukey`, :py:func:`pingouin.pairwise_gameshowell`, :py:func:`pingouin.welch_anova`, :py:func:`pingouin.kruskal`, :py:func:`pingouin.friedman`, :py:func:`pingouin.cochran`, :py:func:`pingouin.remove_rm_na` functions is now ``data`` instead of ``dv`` (to be consistent with other Pingouin functions). This will cause error if the user runs previous Pingouin code with positional-only arguments. As a general rule, **you should always pass keywords arguments** (read more `here <https://treyhunner.com/2018/04/keyword-arguments-in-python/>`_).
   d. For clarity, :py:func:`pingouin.fdr`, :py:func:`pingouin.bonf`, :py:func:`pingouin.holm` have been deprecated from the API and must be called via :py:func:`pingouin.multicomp`.
   e. :py:func:`pingouin.pairwise_ttests` output does not include the ``CLES`` column by default anymore. Users must explicitly pass ``effsize='CLES'``.
   f. The ``remove_na`` argument of :py:func:`pingouin.cronbach_alpha` has been replaced with ``nan_policy`` (`'pairwise'`, or `'listwise'`).
   g. Disabled Travis / AppVeyor testing for Python 3.5 While most functions should work just fine, please note that only Python >3.6 is supported now.

   **New functions**

   a. Added :py:func:`pingouin.harrelldavis`, a robust quantile estimation method (to be used in a future version of the :py:func:`pingouin.plot_shift` function). See `PR63 <https://github.com/raphaelvallat/pingouin/pull/63>`_ by Nicolas Legrand.
   b. The :py:func:`pingouin.ancova` can now directly be used a Pandas method, e.g. ``data.ancova(...)``.
   c. The :py:func:`pingouin.pairwise_tukey` can now directly be used a Pandas method, e.g. ``data.pairwise_tukey(...)``.
   d. Added Sidak one-step correction to :py:func:`pingouin.multicomp` (``method='sidak'``).

   **Enhancements**

   a. Added support for pairwise deletion in :py:func:`pingouin.pairwise_ttests` (default is listwise deletion), using the ``nan_policy`` argument.
   b. Added support for listwise deletion in :py:func:`pingouin.pairwise_corr` (default is pairwise deletion), using the ``nan_policy`` argument.
   c. Added the ``interaction`` boolean argument to :py:func:`pingouin.pairwise_ttests`, useful if one is only interested in the main effects.
   d. Added ``correction_uniform`` boolean argument to :py:func:`pingouin.circ_corrcc`. See `PR64 <https://github.com/raphaelvallat/pingouin/pull/64>`_ by Dominik Straub.

   **Contributors**

   * `Raphael Vallat <https://raphaelvallat.com>`_
   * `Eshin Jolly <http://eshinjolly.com/>`_
   * Nicolas Legrand
   * Dominik Straub


.. dropdown:: **v0.2.8 (July 2019)**

   **Dependencies**

   a. Pingouin now requires SciPy >= 1.3.0 (better handling of tails in :py:func:`pingouin.wilcoxon` function) and Pandas >= 0.24 (fixes a minor bug with 2-way within factor interaction in :py:func:`pingouin.epsilon` with previous version)

   **New functions**

   a. Added :py:func:`pingouin.rcorr` Pandas method to calculate a correlation matrix with r-values on the lower triangle and p-values (or sample size) on the upper triangle.
   b. Added :py:func:`pingouin.tost` function to calculate the two one-sided test (TOST) for equivalence. See `PR51 <https://github.com/raphaelvallat/pingouin/pull/51>`_ by Antoine Weill--Duflos.

   **Enhancements**

   a. :py:func:`pingouin.anova` now works with three or more between factors (requiring statsmodels). One-way ANOVA and balanced two-way ANOVA are computed in pure Pingouin (Python + Pandas) style, while ANOVA with three or more factors, or unbalanced two-way ANOVA are computed using statsmodels.
   b. :py:func:`pingouin.anova` now accepts different sums of squares calculation method for unbalanced N-way design (type 1, 2, or 3).
   c. :py:func:`pingouin.linear_regression` now includes several safety checks to remove duplicate predictors, predictors with only zeros, and predictors with only one unique value (excluding the intercept). This comes at the cost, however, of longer computation time, which is evident when using the :py:func:`pingouin.mediation_analysis` function.
   d. :py:func:`pingouin.mad` now automatically removes missing values and can calculate the mad over the entire array using ``axis=None`` if array is multidimensional.
   e. Better handling of alternative hypotheses in :py:func:`pingouin.wilcoxon`.
   f. Better handling of alternative hypotheses in :py:func:`pingouin.bayesfactor_ttest` (support for 'greater' and 'less').
   g. Better handling of alternative hypotheses in :py:func:`pingouin.ttest` (support for 'greater' and 'less'). This is also taken into account when calculating the Bayes Factor and power of the test.
   h. Better handling of alternative hypotheses in :py:func:`pingouin.power_ttest` and :py:func:`pingouin.power_ttest2n` (support for 'greater' and 'less', and removed 'one-sided').
   i. Implemented a new method to calculate the matched pair rank biserial correlation effect size for :py:func:`pingouin.wilcoxon`, which gives results almost identical to JASP.


.. dropdown:: **v0.2.7 (June 2019)**

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

   **New functions**

   a. Added :py:func:`pingouin.power_rm_anova` function.


.. dropdown:: **v0.2.6 (June 2019)**

   **Bugfixes**

   a. Fixed **major error in two-sided p-value for Wilcoxon test** (:py:func:`pingouin.wilcoxon`), the p-values were accidentally squared, and therefore smaller. Make sure to always use the latest release of Pingouin.
   b. :py:func:`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.)
   c. The ``show_median`` argument of the :py:func:`pingouin.plot_shift` function was not working properly when the percentiles were different that the default parameters.

   **Dependencies**

   a. 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 :py:func:`pingouin.ancova` and :py:func:`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**

   a. Added :py:func:`pingouin.chi2_independence` tests.
   b. Added :py:func:`pingouin.chi2_mcnemar` tests.
   c. Added :py:func:`pingouin.power_chi2` function.
   d. Added :py:func:`pingouin.bayesfactor_binom` function.

   **Enhancements**

   a. :py:func:`pingouin.linear_regression` now returns the residuals.
   b. Completely rewrote :py:func:`pingouin.normality` function, which now support pandas DataFrame (wide & long format), multiple normality tests (:py:func:`scipy.stats.shapiro`, :py:func:`scipy.stats.normaltest`), and an automatic casewise removal of missing values.
   c. Completely rewrote :py:func:`pingouin.homoscedasticity` function, which now support pandas DataFrame (wide & long format).
   d. Faster and more accurate algorithm in :py:func:`pingouin.bayesfactor_pearson` (same algorithm as JASP).
   e. Support for one-sided Bayes Factors in :py:func:`pingouin.bayesfactor_pearson`.
   f. Better handling of required parameters in :py:func:`pingouin.qqplot`.
   g. The epsilon value for the interaction term in :py:func:`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.

   Note that d. and e. also affect the behavior of the :py:func:`pingouin.corr` and :py:func:`pingouin.pairwise_corr` functions.

   **Contributors**

   * `Raphael Vallat <https://raphaelvallat.com>`_
   * `Arthur Paulino <https://github.com/arthurpaulino>`_


.. dropdown:: **v0.2.5 (May 2019)**

   **MAJOR BUG FIXES**

   a. Fixed error in p-values for **one-sample one-sided T-test** (:py:func:`pingouin.ttest`), the two-sided p-value was divided by 4 and not by 2, resulting in inaccurate (smaller) one-sided p-values.
   b. Fixed global error for **unbalanced two-way ANOVA** (:py:func:`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.
   c. The epsilon factor for the interaction term in two-way repeated measures ANOVA (:py:func:`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**

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

   **Enhancements**

   a. The :py:func:`pingouin.rm_corr` function now returns a :py:class:`pandas.DataFrame` with the r-value, degrees of freedom, p-value, confidence intervals and power.
   b. :py:func:`pingouin.compute_esci` now works for paired and one-sample Cohen d.
   c. :py:func:`pingouin.bayesfactor_ttest` and :py:func:`pingouin.bayesfactor_pearson` now return a formatted str and not a float.
   d. :py:func:`pingouin.pairwise_ttests` now returns the degrees of freedom (dof).
   e. Better rounding of float in :py:func:`pingouin.pairwise_ttests`.
   f. Support for wide-format data in :py:func:`pingouin.rm_anova`
   g. :py:func:`pingouin.ttest` now returns the confidence intervals around the difference in means.

   **Missing values**

   a. :py:func:`pingouin.remove_na` and :py:func:`pingouin.remove_rm_na` are now external function documented in the API.
   b. :py:func:`pingouin.remove_rm_na` now works with multiple within-factors.
   c. :py:func:`pingouin.remove_na` now works with 2D arrays.
   d. Removed the `remove_na` argument in :py:func:`pingouin.rm_anova` and :py:func:`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.
   e. The :py:func:`pingouin.ancova` function now applies an automatic listwise deletion of missing values.
   f. Added `remove_na` argument (default = False) in :py:func:`pingouin.linear_regression` and :py:func:`pingouin.logistic_regression` functions
   g. Missing values are automatically removed in the :py:func:`pingouin.anova` function.

   **Contributors**

   * Raphael Vallat
   * Nicolas Legrand


.. dropdown:: **v0.2.4 (April 2019)**

   **Correlation**

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

   **Datasets**

   a. Renamed :py:func:`pingouin.read_dataset` and :py:func:`pingouin.list_dataset` (before one needed to call these functions by calling pingouin.datasets)

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

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

   **Plotting**

   a. Added :py:func:`pingouin.plot_paired` function.

   **Regression**

   a. NaN are now automatically removed in :py:func:`pingouin.mediation_analysis`.
   b. The :py:func:`pingouin.linear_regression` and :py:func:`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.
   c. Added support for multiple parallel mediator in :py:func:`pingouin.mediation_analysis`.
   d. Added support for covariates in :py:func:`pingouin.mediation_analysis`.
   e. Added seed argument to :py:func:`pingouin.mediation_analysis` for reproducible results.
   f. :py:func:`pingouin.mediation_analysis` now returns two-sided p-values computed with a permutation test.
   g. Added :py:func:`pingouin.utils._perm_pval` to compute p-value from a permutation test.

   **Bugs and tests**

   a. Travis and AppVeyor test for Python 3.5, 3.6 and 3.7.
   b. Better doctest & improved examples for many functions.
   c. Fixed bug with :py:func:`pingouin.mad` when axis was not 0.


.. dropdown:: **v0.2.3 (February 2019)**

   **Correlation**

   a. `shepherd` now also returns the outlier vector (same behavior as skipped).
   b. The `corr` function returns the number of outliers for shepherd and skipped.
   c. Removed `mahal` function.

   **Licensing**

   a. Pingouin is now released under the GNU General Public Licence 3.
   b. Added licenses files of external modules (qsturng and tabulate).

   **Plotting**

   a. NaN are automatically removed in qqplot function


.. dropdown:: **v0.2.2 (December 2018)**

   **Plotting**

   a. Started working on Pingouin's plotting module
   b. Added Seaborn and Matplotlib to dependencies
   c. Added plot_skipped_corr function (PR from Nicolas Legrand)
   d. Added qqplot function (Quantile-Quantile plot)
   e. Added plot_blandaltman function (Bland-Altman plot)

   **Power**

   a. Added power_corr, based on the R `pwr` package.
   b. Renamed anova_power and ttest_power to power_anova and power_ttest.
   c. Added power column to corr() and pairwise_corr()
   d. power_ttest function can now solve for sample size, alpha and d
   e. power_ttest2n for two-sample T-test with unequal n.
   f. power_anova can now solve for sample size, number of groups, alpha and eta


.. dropdown:: **v0.2.1 (November 2018)**

   **Effect size**

   a. Separated compute_esci and compute_bootci
   b. Added corrected percentile method and normal approximation to bootstrap
   c. Fixed bootstrapping method


.. dropdown:: **v0.2.0 (November 2018)**

   **ANOVA**

   a. Added Welch ANOVA
   b. Added Games-Howell post-hoc test for one-way ANOVA with unequal variances
   c. Pairwise T-tests now accepts two within or two between factors
   d. Fixed error in padjust correction in the pairwise_ttests function: correction was applied on all p-values at the same time.

   **Correlation/Regression**

   a. Added linear_regression function.
   b. Added logistic_regression function.
   c. Added mediation_analysis function.
   d. Support for advanced indexing (product / combination) in pairwise_corr function.

   **Documentation**

   a. Added Guidelines section with flow charts
   b. Renamed API section to Functions
   c. Major improvements to the documentation of several functions
   d. Added Gitter channel


.. dropdown:: **v0.1.10 (October 2018)**

   **Bug**

   a. Fixed dataset names in MANIFEST.in (.csv files were not copy-pasted with pip)

   **Circular**

   a. Added circ_vtest function

   **Distribution**

   a. Added multivariate_normality function (Henze-Zirkler's Multivariate Normality Test)
   b. Renamed functions test_normality, test_sphericity and test_homoscedasticity to normality, sphericity and homoscedasticity to avoid bugs with pytest.
   c. Moved distribution tests from parametric.py to distribution.py


.. dropdown:: **v0.1.9 (October 2018)**

   **Correlation**

   a. Added partial_corr function (partial correlation)

   **Doc**

   a. Minor improvements in docs and binder notebooks



.. dropdown:: **v0.1.8 (October 2018)**

   **ANOVA**

   a. Added support for multiple covariates in ANCOVA function (requires statsmodels).

   **Documentation**

   a. Major re-organization in API category
   b. Added equations and references for effect sizes and Bayesian functions.

   **Non-parametric**

   a. Added cochran function (Cochran Q test)


.. dropdown:: **v0.1.7 (September 2018)**

   **ANOVA**

   a. Added rm_anova2 function (two-way repeated measures ANOVA).
   b. Added ancova function (Analysis of covariance)

   **Correlations**

   a. Added intraclass_corr function (intraclass correlation).
   b. The rm_corr function uses the new ancova function instead of statsmodels.

   **Datasets**

   a. Added ancova and icc datasets

   **Effect size**

   a. Fixed bug in Cohen d: now use unbiased standard deviation (np.std(ddof=1)) for paired and one-sample Cohen d.
      Please make sure to use pingouin >= 0.1.7 to avoid any mistakes on the paired effect sizes.



.. dropdown:: **v0.1.6 (September 2018)**

   **ANOVA**

   a. Added JNS method to compute sphericity.

   **Bug**

   a. Added .csv datasets files to python site-packages folder
   b. Fixed error in test_sphericity when ddof == 0.



.. dropdown:: **v0.1.5 (August 2018)**

   **ANOVA**

   a. rm_anova, friedman and mixed_anova now require a subject identifier. This avoids improper collapsing when multiple repeated measures factors are present in the dataset.
   b. rm_anova, friedman and mixed_anova now support the presence of other repeated measures factors in the dataset.
   c. Fixed error in test_sphericity
   d. Better output of ANOVA summary
   e. Added epsilon function

   **Code**

   a. Added AppVeyor CI (Windows)
   b. Cleaned some old functions

   **Correlation**

   a. Added repeated measures correlation (Bakdash and Marusich 2017).
   b. Added robust skipped correlation (Rousselet and Pernet 2012).
   c. Pairwise_corr function now automatically delete non-numeric columns.

   **Dataset**

   a. Added pingouin.datasets module (read_dataset & list_dataset functions)
   b. Added datasets: bland1995, berens2009, dolan2009, mcclave1991

   **Doc**

   a. Examples are now Jupyter Notebooks.
   b. Binder integration

   **Misc**

   a. Added median absolute deviation (mad)
   b. Added mad median rule (Wilcox 2012)
   c. Added mahal function (equivalent of Matlab mahal function)

   **Parametric**

   a. Added two-way ANOVA.
   b. Added pairwise_tukey function



.. dropdown:: **v0.1.4 (July 2018)**

   **Installation**

   a. Fix bug with pip install caused by pingouin.external

   **Circular statistics**

   a. Added circ_corrcc, circ_corrcl, circ_r, circ_rayleigh


.. dropdown:: **v0.1.3 (June 2018)**

   **Documentation**

   a. Added several tutorials
   b. Improved doc of several functions

   **Bayesian**

   a. T-test now reports the Bayes factor of the alternative hypothesis (BF10)
   b. Pearson correlation now reports the Bayes factor of the alternative hypothesis (BF10)

   **Non-parametric**

   a. Kruskal-Wallis test
   b. Friedman test

   **Correlations**

   a. Added Shepherd's pi correlation (Schwarzkopf et al. 2012)
   b. Fixed bug in confidence intervals of correlation coefficients
   c. Parametric 95% CI are returned by default when calling corr


.. dropdown:: **v0.1.2 (June 2018)**

   **Correlation**

   a. Pearson
   b. Spearman
   c. Kendall
   d. Percentage bend (robust)
   e. Pairwise correlations between all columns of a pandas dataframe

   **Non-parametric**

   a. Mann-Whitney U
   b. Wilcoxon signed-rank
   c. Rank-biserial correlation effect size
   d. Common language effect size


.. dropdown:: **v0.1.1 (April 2018)**

   **ANOVA**

   a. One-way
   b. One-way repeated measures
   c. Two-way split-plot (one between factor and one within factor)

   **Miscellaneous statistical functions**

   a. T-tests
   b. Power of T-tests and one-way ANOVA


.. dropdown:: **v0.1.0 (April 2018)**

   Initial release.

   **Pairwise comparisons**

   a. FDR correction (BH / BY)
   b. Bonferroni
   c. Holm

   **Effect sizes**:

   a. Cohen's d (independent and repeated measures)
   b. Hedges g
   c. Glass delta
   d. Eta-square
   e. Odds-ratio
   f. Area Under the Curve

   **Miscellaneous statistical functions**

   a. Geometric Z-score
   b. Normality, sphericity homoscedasticity and distributions tests

   **Code**

   a. PEP8 and Flake8
   b. Tests and code coverage