# Functions

## ANOVA and T-test

 anova([data, dv, between, ss_type, …]) One-way and N-way ANOVA. ancova([data, dv, between, covar, effsize]) ANCOVA with one or more covariate(s). rm_anova([data, dv, within, subject, …]) One-way and two-way repeated measures ANOVA. epsilon(data[, dv, within, subject, correction]) Epsilon adjustement factor for repeated measures. mixed_anova([data, dv, within, subject, …]) Mixed-design (split-plot) ANOVA. welch_anova([data, dv, between]) One-way Welch ANOVA. tost(x, y[, bound, paired, correction]) Two One-Sided Test (TOST) for equivalence. ttest(x, y[, paired, tail, correction, r]) T-test.

## Bayesian

 bayesfactor_binom(k, n[, p]) Bayes factor of a binomial test with $$k$$ successes, $$n$$ trials and base probability $$p$$. bayesfactor_ttest(t, nx[, ny, paired, tail, r]) Bayes Factor of a T-test. bayesfactor_pearson(r, n[, tail, method, kappa]) Bayes Factor of a Pearson correlation.

## Circular

 convert_angles(angles[, low, high, positive]) Element-wise conversion of arbitrary-unit circular quantities to radians. circ_axial(angles, n) Transforms n-axial data to a common scale. circ_corrcc(x, y[, tail, correction_uniform]) Correlation coefficient between two circular variables. circ_corrcl(x, y[, tail]) Correlation coefficient between one circular and one linear variable random variables. circ_mean(angles[, w, axis]) Mean direction for (binned) circular data. circ_r(angles[, w, d, axis]) Mean resultant vector length for circular data. circ_rayleigh(angles[, w, d]) Rayleigh test for non-uniformity of circular data. circ_vtest(angles[, dir, w, d]) V test for non-uniformity of circular data with a specified mean direction.

## Contingency

 chi2_independence(data, x, y[, correction]) Chi-squared independence tests between two categorical variables. chi2_mcnemar(data, x, y[, correction]) Performs the exact and approximated versions of McNemar’s test. dichotomous_crosstab(data, x, y) Generates a 2x2 contingency table from a pandas.DataFrame that contains only dichotomous entries, which are converted to 0 or 1.

## Correlation and regression

 corr(x, y[, tail, method]) (Robust) correlation between two variables. pairwise_corr(data[, columns, covar, tail, …]) Pairwise (partial) correlations between columns of a pandas dataframe. partial_corr([data, x, y, covar, x_covar, …]) Partial and semi-partial correlation. pcorr(self) Partial correlation matrix (pandas.DataFrame method). rcorr(self[, method, upper, decimals, …]) Correlation matrix of a dataframe with p-values and/or sample size on the upper triangle (pandas.DataFrame method). distance_corr(x, y[, tail, n_boot, seed]) Distance correlation between two arrays. rm_corr([data, x, y, subject, tail]) Repeated measures correlation. linear_regression(X, y[, add_intercept, …]) (Multiple) Linear regression. logistic_regression(X, y[, coef_only, …]) (Multiple) Binary logistic regression. mediation_analysis([data, x, m, y, covar, …]) Mediation analysis using a bias-correct non-parametric bootstrap method.

## Distribution

 anderson(*args[, dist]) Anderson-Darling test of distribution. Geometric standard (Z) score. homoscedasticity(data[, dv, group, method, …]) Test equality of variance. normality(data[, dv, group, method, alpha]) Univariate normality test. sphericity(data[, dv, within, subject, …]) Mauchly and JNS test for sphericity.

## Effect sizes

 compute_effsize(x, y[, paired, eftype]) Calculate effect size between two set of observations. compute_effsize_from_t(tval[, nx, ny, N, eftype]) Compute effect size from a T-value. convert_effsize(ef, input_type, output_type) Conversion between effect sizes. compute_esci([stat, nx, ny, paired, eftype, …]) Parametric confidence intervals around a Cohen d or a correlation coefficient. compute_bootci(x[, y, func, method, paired, …]) Bootstrapped confidence intervals of univariate and bivariate functions.

## Multiple comparisons and post-hoc tests

 pairwise_corr(data[, columns, covar, tail, …]) Pairwise (partial) correlations between columns of a pandas dataframe. pairwise_ttests([data, dv, between, within, …]) Pairwise T-tests. pairwise_tukey([data, dv, between, effsize]) Pairwise Tukey-HSD post-hoc test. pairwise_gameshowell([data, dv, between, …]) Pairwise Games-Howell post-hoc test. multicomp(pvals[, alpha, method]) P-values correction for multiple comparisons.

## Multivariate tests

 multivariate_normality(X[, alpha]) Henze-Zirkler multivariate normality test. multivariate_ttest(X[, Y, paired]) Hotelling T-squared test (= multivariate T-test)

## Non-parametric

 cochran([data, dv, within, subject]) Cochran Q test. friedman([data, dv, within, subject]) Friedman test for repeated measurements. kruskal([data, dv, between, detailed]) Kruskal-Wallis H-test for independent samples. mad(a[, normalize, axis]) Median Absolute Deviation (MAD) along given axis of an array. Robust outlier detection based on the MAD-median rule. mwu(x, y[, tail]) Mann-Whitney U Test (= Wilcoxon rank-sum test). wilcoxon(x, y[, tail]) Wilcoxon signed-rank test. harrelldavis(x[, quantile, axis]) Harrell-Davis robust estimate of the $$q^{th}$$ quantile(s) of the data.

## Others

 print_table(df[, floatfmt, tablefmt]) Pretty display of table. remove_na(x[, y, paired, axis]) Remove missing values along a given axis in one or more (paired) numpy arrays. remove_rm_na([data, dv, within, subject, …]) Remove missing values in long-format repeated-measures dataframe. read_dataset(dname) Read example datasets. List available example datasets. Reset Pingouin’s default global options (e.g.

## Plotting

 plot_blandaltman(x, y[, agreement, …]) Generate a Bland-Altman plot to compare two sets of measurements. plot_circmean(angles[, figsize, dpi, ax, …]) Plot the circular mean and vector length of a set of angles on the unit circle. plot_paired([data, dv, within, subject, …]) Paired plot. plot_shift(x, y[, paired, n_boot, …]) Shift plot. plot_rm_corr([data, x, y, subject, legend, …]) Plot a repeated measures correlation. qqplot(x[, dist, sparams, confidence, …]) Quantile-Quantile plot.

## Power analysis

 power_anova([eta, k, n, power, alpha]) Evaluate power, sample size, effect size or significance level of a one-way balanced ANOVA. power_rm_anova([eta, m, n, power, alpha, …]) Evaluate power, sample size, effect size or significance level of a balanced one-way repeated measures ANOVA. power_chi2(dof[, w, n, power, alpha]) Evaluate power, sample size, effect size or significance level of chi-squared tests. power_corr([r, n, power, alpha, tail]) Evaluate power, sample size, correlation coefficient or significance level of a correlation test. power_ttest([d, n, power, alpha, contrast, tail]) Evaluate power, sample size, effect size or significance level of a one-sample T-test, a paired T-test or an independent two-samples T-test with equal sample sizes. power_ttest2n(nx, ny[, d, power, alpha, tail]) Evaluate power, effect size or significance level of an independent two-samples T-test with unequal sample sizes.

## Reliability and consistency

 cronbach_alpha([data, items, scores, …]) Cronbach’s alpha reliability measure. intraclass_corr([data, targets, raters, …]) Intraclass correlation.