Statistical package in Python based on Pandas
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).
This release requires pandas≥1.5. We recommend scipy≥1.11.0.
corr()
: fix CI95%
column name in returned dataframe by @kraktus in https://github.com/raphaelvallat/pingouin/pull/382
Full Changelog: https://github.com/raphaelvallat/pingouin/compare/v0.5.3...v0.5.4
This is a minor release with a few bugfixes, several improvements and one new function/pandas.DataFrame method. Read the changelog at https://pingouin-stats.org/changelog.html
Bugfixes
a. 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 #251, this behavior is not valid. In one-way ANOVA design, the eta-squared should be equal to the generalized eta-squared. As of March 2022, this bug is also present in JASP. We have therefore updated the unit tests to use JAMOVI instead.
Please double check any effect sizes previously obtained with the pingouin.rm_anova
function!
b. Fixed invalid resampling behavior for bivariate functions in pingouin.compute_bootci
when x and y were not paired. #281
c. Fixed bug where confidence
(previously ci
) was ignored when calculating the bootstrapped confidence intervals in pingouin.plot_shift
. #282
Enhancements
a. The pingouin.pairwise_ttests
has been renamed to pingouin.pairwise_tests
. Non-parametric tests are also supported in this function with the parametric=False
argument, and thus the name "ttests" was misleading #209
b. Allow pingouin.bayesfactor_binom
to take Beta alternative model. #252
c. Allow keyword arguments for logistic regression in pingouin.mediation_analysis
. #245
d. Speed improvements for the Holm and FDR correction in pingouin.multicomp
. #271
e. Speed improvements univariate functions in pingouin.compute_bootci
(e.g. func="mean"
is now vectorized).
f. Rename eta
to eta_squared
in pingouin.power_anova
andpingouin.power_rm_anova
to avoid any confusion. #280
g. Add support for DataMatrix objects. #286
h. Use black for code formatting.
This is a minor release, with several bugfixes and improvements. This release is compatible with SciPy 1.8 and Pandas 1.4.
Bugfixes
Enhancements
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!
This is a major release with several important bugfixes. We recommend all users to upgrade to this new version.
See the full changelog at: https://pingouin-stats.org/changelog.html#v0-5-0-october-2021
This is a major release with an important upgrade of the dependencies (requires Python 3.7+ and SciPy 1.7+), several enhancements in existing function and a new function to test the equality of covariance matrices (pingouin.box_m). We recommend all users to upgrade to the latest version of Pingouin.
See the full changelog at: https://pingouin-stats.org/changelog.html#v0-4-0-august-2021
This release fixes a critical error in 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 https://github.com/raphaelvallat/pingouin/issues/171.
For the full changelog, please see https://pingouin-stats.org/changelog.html
This is a minor release with several bug fixes in pingouin.corr. The full changelog can be found here.
This release fixes an error in the calculation of the p-values in the pg.pairwise_tukey() and pg.pairwise_gameshowell() functions (https://github.com/raphaelvallat/pingouin/pull/156). 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 uses statsmodels to estimate the p-values.
See changelog at: https://pingouin-stats.org/changelog.html