Releases | Open Source Science

Pingouin - v0.5.5

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

What's Changed

Fix penalty for LogisticRegression by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/403
Switch to modern python packaging by @getzze in https://github.com/raphaelvallat/pingouin/pull/406
Remove call to sns.despine by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/410
Updated deprecated function by @sjg2203 in https://github.com/raphaelvallat/pingouin/pull/414
Add errstate(divide="ignore") in Bayes Factor calculation by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/415
Remove inplace on single column by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/423
Fix RBC sign in mwu by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/424
Overhaul documentation (pydatasphinxtheme) by @yann1cks in https://github.com/raphaelvallat/pingouin/pull/432
Release 0.5.5 by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/436

New Contributors

@getzze made their first contribution in https://github.com/raphaelvallat/pingouin/pull/406
@sjg2203 made their first contribution in https://github.com/raphaelvallat/pingouin/pull/414
@yann1cks made their first contribution in https://github.com/raphaelvallat/pingouin/pull/432

Full Changelog: https://github.com/raphaelvallat/pingouin/compare/v0.5.4...v0.5.5

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 1 year ago

Pingouin - v0.5.4

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.

What's Changed

Minor typo fix in docs by @musicinmybrain in https://github.com/raphaelvallat/pingouin/pull/329
clip r values by @remrama in https://github.com/raphaelvallat/pingouin/pull/342
fix: deprecated parameter by @bitsnaps in https://github.com/raphaelvallat/pingouin/pull/341
hotfix: CI crash in testpowerchi2 [WIP] by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/344
hotfix: plotrmcorr crash with specific column names by @remrama in https://github.com/raphaelvallat/pingouin/pull/351
Add check for noncentrality parameters. by @agkphysics in https://github.com/raphaelvallat/pingouin/pull/347
Use pyupgrade by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/364
fix groupby.mean for only numeric values by @jajcayn in https://github.com/raphaelvallat/pingouin/pull/363
Function test fails for np.mean by @gedeck in https://github.com/raphaelvallat/pingouin/pull/380
Fix in flatten_list for Python 3.12 by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/370
corr(): fix CI95% column name in returned dataframe by @kraktus in https://github.com/raphaelvallat/pingouin/pull/382
Replace None in dataset to fix unit tests by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/388
Remove outdated + bump pandas 1.5 by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/389
Fix doctests by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/390
Fix warnings by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/391
Remove non-centrality check (solved in scipy 1.11) by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/392
Use numeric_only=True in DataFrame.corr() and cov() by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/393
Add numeric_only=True in remaining pandas functions by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/396
Release 0.5.4 by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/397

New Contributors

@musicinmybrain made their first contribution in https://github.com/raphaelvallat/pingouin/pull/329
@bitsnaps made their first contribution in https://github.com/raphaelvallat/pingouin/pull/341
@agkphysics made their first contribution in https://github.com/raphaelvallat/pingouin/pull/347
@jajcayn made their first contribution in https://github.com/raphaelvallat/pingouin/pull/363
@kraktus made their first contribution in https://github.com/raphaelvallat/pingouin/pull/382

Full Changelog: https://github.com/raphaelvallat/pingouin/compare/v0.5.3...v0.5.4

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 2 years ago

Pingouin - v0.5.3

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

What's Changed

Fix numerical stability issue in multivariate_normality by @gkanwar in https://github.com/raphaelvallat/pingouin/pull/292
Add new function for pairwise T-tests between columns of a dataframe (pingouin.ptests) by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/291
Handle single-sample comparsion in pairwise_test by @George3d6 in https://github.com/raphaelvallat/pingouin/pull/299
Change TestRegression class test methods to fix victim flakiness by @blazyy in https://github.com/raphaelvallat/pingouin/pull/303
Add aesthetic flexibility to plotrmcorr by @remrama in https://github.com/raphaelvallat/pingouin/pull/312
Update distribution.py by @ALL-SPACE-Rob in https://github.com/raphaelvallat/pingouin/pull/310
Plotting seaborn.FacetGrid compatibility by @remrama in https://github.com/raphaelvallat/pingouin/pull/314
Use scikit-learn>=1.1.2 by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/300
Plot shift documentation PR by @turkalpmd in https://github.com/raphaelvallat/pingouin/pull/320
Fix pandas warning by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/323
Deal with small sample size in pingouin.normality when using long-format by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/324
Renamed 'r' with 'pointbiserialr' in convert_effsize by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/325
Exact calculation of effect sizes in pairwisetukey and pairwisegameshowell by @raphaelvallat in https://github.com/raphaelvallat/pingouin/pull/328

New Contributors

@gkanwar made their first contribution in https://github.com/raphaelvallat/pingouin/pull/292
@George3d6 made their first contribution in https://github.com/raphaelvallat/pingouin/pull/299
@blazyy made their first contribution in https://github.com/raphaelvallat/pingouin/pull/303
@remrama made their first contribution in https://github.com/raphaelvallat/pingouin/pull/312
@ALL-SPACE-Rob made their first contribution in https://github.com/raphaelvallat/pingouin/pull/310
@turkalpmd made their first contribution in https://github.com/raphaelvallat/pingouin/pull/320

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 3 years ago

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.rmanova` 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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 4 years ago

Pingouin - v0.5.1

Pingouin 0.5.1

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

Bugfixes

Added support for SciPy 1.8 and Pandas 1.4. https://github.com/raphaelvallat/pingouin/pull/234
Fixed bug where pingouin.rm_anova() and pingouin.mixed_anova() changed the dtypes of categorical columns in-place https://github.com/raphaelvallat/pingouin/issues/224

Enhancements

Faster implementation of pingouin.gzscore(), adding all options available in zscore: axis, ddof and nan_policy. Warning: this function is deprecated and will be removed in the next version of Pingouin (use scipy.stats.gzscore() instead). https://github.com/raphaelvallat/pingouin/pull/210.
Replace use of statsmodels’ studentized range distribution functions with more SciPy’s more accurate scipy.stats.studentized_range(). https://github.com/raphaelvallat/pingouin/pull/229.
Add support for optional keywords argument in the pingouin.homoscedasticity() function https://github.com/raphaelvallat/pingouin/issues/218
Add support for the Jarque-Bera test in pingouin.normality() https://github.com/raphaelvallat/pingouin/issues/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!

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 4 years ago

Pingouin - v0.5.0

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 4 years ago

Pingouin - v0.4.0

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 5 years ago

Pingouin - v0.3.12

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat about 5 years ago

Pingouin - v0.3.11

This is a minor release with several bug fixes in pingouin.corr. The full changelog can be found here.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat about 5 years ago

Pingouin - v0.3.10

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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 5 years ago

Pingouin - v0.3.9

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 5 years ago

Pingouin - 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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 5 years ago

Pingouin - v0.3.7

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 6 years ago

Pingouin - v0.3.6

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 6 years ago

Pingouin - v0.3.5

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 6 years ago

Pingouin - v0.3.4

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat about 6 years ago

Pingouin - 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.pairwisettests when using ``returndesc=Trueis now calculated withnp.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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 6 years ago

Pingouin - 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.plotrmcorr() 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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 6 years ago

Pingouin - 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 https://github.com/raphaelvallat/pingouin/issues/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 https://github.com/raphaelvallat/pingouin/issues/73

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 6 years ago

Pingouin - v0.3.0

New functions

Added pingouin.plotrmcorr() 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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 6 years ago

Pingouin - v0.2.9

Minor release with mostly internal code refactoring.

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat over 6 years ago

Pingouin - v0.2.8

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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 7 years ago

Pingouin - v0.2.7

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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 7 years ago

Pingouin - v0.2.6

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 showmedian argument of the `pingouin.plotshift()` 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

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat almost 7 years ago

Pingouin - v0.2.5

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.bayesfactorttest() and pingouin.bayesfactorpearson() 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.removena() and pingouin.removerm_na() are now external function documented in the API.
pingouin.removermna() now works with multiple within-factors.
pingouin.remove_na() now works with 2D arrays.
Removed the removena argument in pingouin.rmanova() 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 removena argument (default = False) in pingouin.linearregression() and pingouin.logistic_regression() functions
Missing values are automatically removed in the pingouin.anova() function.

Contributors

Raphael Vallat
Nicolas Legrand

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat about 7 years ago

Pingouin - v0.2.4

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.readdataset() and pingouin.listdataset() (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.linearregression() and pingouin.logisticregression() 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.permpval() 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.

Scientific Software - Peer-reviewed - Python
Published by raphaelvallat about 7 years ago