This package is considered a duplicate. The official version of this package is found at:https://kassambara.r-universe.dev/rstatix

Package: rstatix 1.1.0

Alboukadel Kassambara

rstatix: Pipe-Friendly Framework for Basic Statistical Tests

Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. The output of each test is automatically transformed into a tidy data frame to facilitate visualization. Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix. Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 'Cramer V' for the association between categorical variables. The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.

Authors:Alboukadel Kassambara [aut, cre]

rstatix_1.1.0.tar.gz
rstatix_1.1.0.zip(r-4.7-any)
rstatix_1.1.0.tar.gz(r-4.7-any)rstatix_1.1.0.tar.gz(r-4.6-any)
rstatix_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rstatix/json (API)

# Install 'rstatix' in R:

install.packages('rstatix', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kassambara/rstatix/issues

Pkgdown/docs site:https://rpkgs.datanovia.com

On CRAN:

12.93 score 1 stars 510 packages 15k scripts 240k downloads 123 mentions 139 exports 69 dependencies

Last updated from:6a12172700. Checks:5 OK. Indexed: no.

Target	Result	Time
linux-devel-x86_64	OK	280
source / vignettes	OK	254
linux-release-x86_64	OK	281
windows-devel-x86_64	OK	225
wasm-release	OK	141

Exports:%>%add_cld add_significance add_x_position add_xy_position add_y_position adjust_pvalue Anova anova_summary anova_test as_cor_mat augment binom_test box_m check_test_assumptions chisq_descriptives chisq_test cliff_delta cochran_qtest cohens_d conover_test convert_as_factor cor_as_symbols cor_gather cor_get_pval cor_mark_significant cor_mat cor_plot cor_pmat cor_reorder cor_select cor_spread cor_test counts_to_cases cramer_v create_test_label desc df_arrange df_get_var_names df_group_by df_label_both df_label_value df_nest_by df_select df_split_by df_unite df_unite_factors doo drop_na dunn_test dunnett_test emmeans_test eta_squared expected_freq factorial_design filter fisher_test fligner_test freq_table friedman_conover_test friedman_effsize friedman_nemenyi_test friedman_test games_howell_test gather get_anova_table get_comparisons get_description get_emmeans get_mode get_n get_pwc_label get_summary_stats get_test_label get_y_position glance group_by identify_outliers is_extreme is_outlier kruskal_effsize kruskal_test ks_test levene_test mahalanobis_distance make_clean_names Manova mcnemar_test mshapiro_test multinom_test mutate observed_freq omega_squared p_adj_names p_detect p_format p_mark_significant p_names p_round pairwise_binom_test pairwise_binom_test_against_p pairwise_chisq_gof_test pairwise_chisq_test_against_p pairwise_fisher_test pairwise_mcnemar_test pairwise_prop_test pairwise_sign_test pairwise_t_test pairwise_wilcox_test partial_eta_squared partial_omega_squared pearson_residuals posthoc_test prop_test prop_trend_test pull_lower_triangle pull_triangle pull_upper_triangle remove_ns reorder_levels replace_lower_triangle replace_triangle replace_upper_triangle row_wise_fisher_test row_wise_prop_test sample_n_by select set_ref_level shapiro_test sign_test spread std_residuals t_test tibble tidy tukey_hsd welch_anova_test wilcox_effsize wilcox_test

Dependencies:abind backports boot broom car carData cli colorspace corrplot cowplot cpp11 Deriv doBy dplyr farver forecast Formula fracdiff generics ggplot2 glue gtable isoband labeling lattice lifecycle lme4 lmtest magrittr MASS Matrix MatrixModels mgcv minqa modelr nlme nloptr nnet numDeriv pbkrtest pillar pkgconfig purrr quantreg R6 rbibutils RColorBrewer Rcpp RcppArmadillo RcppEigen Rdpack reformulas rlang S7 scales SparseM stringi stringr survival tibble tidyr tidyselect timeDate urca utf8 vctrs viridisLite withr zoo

Get started with rstatix

Rendered fromrstatix.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-07-23

Help page	Topics
Compact Letter Display of All-Pairwise Comparisons	add_cld
Add P-value Significance Symbols	add_significance
Adjust P-values for Multiple Comparisons	adjust_pvalue
Create Nice Summary Tables of ANOVA Results	anova_summary
Anova Test	anova_test get_anova_table plot.anova_test print.anova_test
Convert a Correlation Test Data Frame into a Correlation Matrix	as_cor_mat
Exact Binomial Test	binom_test pairwise_binom_test pairwise_binom_test_against_p
Box's M-test for Homogeneity of Covariance Matrices	box_m
Check One-Way Assumptions and Recommend the Test	check_test_assumptions
Chi-squared Test for Count Data	chisq_descriptives chisq_test expected_freq observed_freq pairwise_chisq_gof_test pairwise_chisq_test_against_p pearson_residuals std_residuals
Cliff's Delta Effect Size for Ordinal / Non-parametric Comparisons	cliff_delta
Cochran's Q Test	cochran_qtest
Compute Cohen's d Measure of Effect Size	cohens_d
Conover's All-Pairs Rank Comparison Test	conover_test
Factors	convert_as_factor reorder_levels set_ref_level
Replace Correlation Coefficients by Symbols	cor_as_symbols
Reshape Correlation Data	cor_gather cor_spread
Add Significance Levels To a Correlation Matrix	cor_mark_significant
Compute Correlation Matrix with P-values	cor_get_pval cor_mat cor_pmat
Visualize Correlation Matrix Using Base Plot	cor_plot
Reorder Correlation Matrix	cor_reorder
Subset Correlation Matrix	cor_select
Correlation Test	cor_test
Convert a Table of Counts into a Data Frame of cases	counts_to_cases
Compute Cramer's V	cramer_v
Arrange Rows by Column Values	df_arrange
Get User Specified Variable Names	df_get_var_names
Group a Data Frame by One or more Variables	df_group_by
Functions to Label Data Frames by Grouping Variables	df_label_both df_label_value
Nest a Tibble By Groups	df_nest_by
Select Columns in a Data Frame	df_select
Split a Data Frame into Subset	df_split_by
Unite Multiple Columns into One	df_unite df_unite_factors
Alternative to dplyr::do for Doing Anything	doo
Dunn's Test of Multiple Comparisons	dunn_test
Dunnett's Many-to-One Comparisons Test	dunnett_test
Pairwise Comparisons of Estimated Marginal Means	emmeans_test get_emmeans
Effect Size for ANOVA	eta_squared partial_eta_squared
Build Factorial Designs for ANOVA	factorial_design
Fisher's Exact Test for Count Data	fisher_test pairwise_fisher_test row_wise_fisher_test
Fligner-Killeen Test	fligner_test
Compute Frequency Table	freq_table
Conover's All-Pairs Comparisons Test for Friedman Rank Sums	friedman_conover_test
Friedman Test Effect Size (Kendall's W Value)	friedman_effsize
Nemenyi Post-Hoc Test for Friedman Rank Sums	friedman_nemenyi_test
Friedman Rank Sum Test	friedman_test
Games Howell Post-hoc Tests	games_howell_test
Create a List of Possible Comparisons Between Groups	get_comparisons
Compute Mode	get_mode
Extract Label Information from Statistical Tests	create_test_label get_description get_n get_pwc_label get_test_label
Compute Summary Statistics	get_summary_stats
Autocompute P-value Positions For Plotting Significance	add_xy_position add_x_position add_y_position get_y_position
Identify Univariate Outliers Using Boxplot Methods	identify_outliers is_extreme is_outlier
Kruskal-Wallis Effect Size	kruskal_effsize
Kruskal-Wallis Test	kruskal_test
Two-Sample Kolmogorov-Smirnov Test	ks_test
Levene's Test	levene_test
Compute Mahalanobis Distance and Flag Multivariate Outliers	mahalanobis_distance
Make Clean Names	make_clean_names
McNemar's Chi-squared Test for Count Data	mcnemar_test pairwise_mcnemar_test
Exact Multinomial Test	multinom_test
Omega Squared for ANOVA	omega_squared partial_omega_squared
Rounding and Formatting p-values	p_adj_names p_detect p_format p_mark_significant p_names p_round
Choose and Run the Appropriate Post-Hoc Test	posthoc_test print.posthoc_test
Proportion Test	pairwise_prop_test prop_test row_wise_prop_test
Test for Trend in Proportions	prop_trend_test
Pull Lower and Upper Triangular Part of a Matrix	pull_lower_triangle pull_triangle pull_upper_triangle
Remove Non-Significant from Statistical Tests	remove_ns
Replace Lower and Upper Triangular Part of a Matrix	replace_lower_triangle replace_triangle replace_upper_triangle
Programming with rstatix (tidy evaluation)	rstatix-programming
References and related packages	rstatix-references
Sample n Rows By Group From a Table	sample_n_by
Shapiro-Wilk Normality Test	mshapiro_test shapiro_test
Sign Test	pairwise_sign_test sign_test
T-test	pairwise_t_test t_test
Tidy an rstatix Test Result	glance.rstatix_test tidy.rstatix_test
Tukey Honest Significant Differences	tukey_hsd tukey_hsd.data.frame tukey_hsd.default tukey_hsd.lm
Welch One-Way ANOVA Test	welch_anova_test
Wilcoxon Effect Size	wilcox_effsize
Wilcoxon Tests	pairwise_wilcox_test wilcox_test

Package: rstatix 1.1.0

rstatix: Pipe-Friendly Framework for Basic Statistical Tests

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Help Manual

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