v0.3.9

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

v0.3.8

• Important bugfix in pingouin.ttest() in which the 95% confidence intervals for one-sample T-test with y != 0 were invalid.
• Added an "options" module to control global rounding/display behavior.
• Several enhancements / new features in existing functions.

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

v0.3.7

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

v0.3.6

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

v0.3.5

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

v0.3.4

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

v0.3.3

Minor release:

Bugfixes

• Fixed a bug in 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.
• The standard deviation in 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

• Added the pingouin.plot_circmean function to plot the circular mean and circular vector length of a set of angles (in radians) on the unit circle. Note that this function is still in beta and some parameters may change without warnings in the next releases.

v0.3.2

Hotfix release to fix a critical issue with 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

• MAJOR: Fixed a bug in 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 pingouin.mixed_anova() function. The old behavior of Pingouin can still be obtained using the marginal=False argument.

• Minor: Added a check in 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]).

• Minor: Fixed a bug which caused the pingouin.plot_rm_corr() and 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

• Upon loading, Pingouin will now use the outdated package to check and warn the user if a newer stable version is available.

• 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.

• Added the correction argument to pingouin.pairwise_ttests() to enable or disable Welch’s correction for independent T-tests.

v0.3.1

Minor release with some bugfixes

• Fixed a bug in which missing values were removed from all columns in the dataframe in pingouin.kruskal(), even columns that were unrelated. See #74.

• The 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 pingouin.pairwise_corr() function.

• Fixed quantile direction in the 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

New functions

• Added pingouin.plot_rm_corr() to plot a repeated measures correlation

Enhancements

• Added the relimp argument to 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() 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.
• The 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() to support 2D arrays.