- New function to extract information from rstatix statistical tests: -
`get_n()`

to extract sample count (n) from statistical test results. -`get_description`

to extract stat test description or name -`remove_ns()`

to remove non-significant rows.

- Rewritting
`add_x_position()`

to better support different situations (#73). - Now, the output of the function
`dunn_test()`

include`estimate1`

and`estimate2`

when the argument`detailed = TRUE`

is specified. The`estimate1`

and`estimate2`

values represent the mean rank values of the two groups being compared, respectively (#59).

`cor_spread()`

doc updated, error is explicitly shown if the input data doesn’t contain the columns “var1”, “var2” and “cor” (#95)- Maintenance updates of the functions
`emmeans_test()`

and`levene_test()`

to adapt to broom release 0.7.4 (#89) - The documentation of the function
`anova_test()`

is updated to explain the internal contrast setting (#74). - Now,
`p_mark_significance()`

works when all p-values are NA. Empty character ("") is returned for NA (#64). - Classes (
`rstatix`

and`grouped_anova_test`

) added to grouped ANOVA test (#61) - New argument
`scales`

added in the function`get_y_position()`

. If the specified value is “free” or “free_y”, then the step increase of y positions will be calculated by plot panels. Note that, using “free” or “free_y” gives the same result. A global step increase is computed when scales = “fixed” (#56).

- The function
`anova_test()`

computes now repeated measures ANOVA without error when unused columns are present in the input data frame (#55)

- Adapted to upcoming broom v0.7.0 release (#49)
- New argument
`stack`

added in`get_y_position()`

to compute p-values y position for stacked bar plots (#48). `wilcox_test()`

: Now, if`detailed = TRUE`

, an estimate of the location parameter (Only present if argument detailed = TRUE). This corresponds to the pseudomedian (for one-sample case) or to the difference of the location parameter (for two-samples case) (#45).

`anova_test()`

function: Changing R default contrast setting (`contr.treatment`

) into orthogonal contrasts (`contr.sum`

) to have comparable results to SPSS when users define the model using formula (@benediktclaus, #40).- Now, the option
`type = "quantile"`

of`get_summary_stats()`

works properly (@Boyoron, #39).

- New functions added for easy data frame manipulation. These functions are internally used in the
`rstatix`

and the`ggpubr`

package and makes it easy to program with tidyverse packages using non standard evaluation. - df_select - df_arrange - df_group_by - df_nest_by - df_split_by - df_unite - df_get_var_names - df_label_both - df_label_value

- Now, in
`freq_table()`

the option`na.rm`

removes only missing values in the variables used to create the frequency table (@JuhlinF, #25). - Missing values are now correctly handled in
`anova_test()`

(@benediktclaus, #31) - Maintenance for adapting to the future dplyr 1.0.0 version #32

- An informative message is now displayed when users try to apply Hedge’s correction when computing the Cohen’s D for one sample test (@GegznaV, #36).
- Bug fixes in the
`games_howell_test()`

function : the t-statistic is now calculated using the**absolute**mean difference between groups (@GegznaV, #37). - x position is now correctly computed when when making custom comparisons (@barrel0luck, #28).

- The
`cohens_d()`

function now supports Hedge’s correction. New argument`hedge.correction`

added . logical indicating whether apply the Hedges correction by multiplying the usual value of Cohen’s d by`(N-3)/(N-2.25)`

(for unpaired t-test) and by`(n1-2)/(n1-1.25)`

for paired t-test; where N is the total size of the two groups being compared (N = n1 + n2) (@IndrajeetPatil, #9).

- Now, the function
`cohens_d()`

outputs values with directionality. The absolute value is no longer returned. It can now be positive or negative depending on the data (@narunpat, #9).

- The value of
`mu`

is now considered when calculating`cohens_d()`

for one sample t-test (@mllewis, #22). - The function
`tukey_hsd()`

now handles situation where minus`-`

symbols are present in factor levels (@IndrajeetPatil, #19).

- tidyr > 1.0.0 now required
- know,
`identify_outliers`

returns a basic data frame instead of tibble when nrow = 0 (for nice printing) - new argument
`detailed`

added in`dunn_test()`

. If TRUE, then estimate and method columns are shown in the results.

`prop_test()`

,`pairwise_prop_test()`

and`row_wise_prop_test()`

. Performs one-sample and two-samples z-test of proportions. Wrappers around the R base function`prop.test()`

but have the advantage of performing pairwise and row-wise z-test of two proportions, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.`fisher_test()`

,`pairwise_fisher_test()`

and`row_wise_fisher_test()`

: Fisher’s exact test for count data. Wrappers around the R base function`fisher.test()`

but have the advantage of performing pairwise and row-wise fisher tests, the post-hoc tests following a significant chi-square test of homogeneity for 2xc and rx2 contingency tables.`chisq_test()`

,`pairwise_chisq_gof_test()`

,`pairwise_chisq_test_against_p()`

: Chi-square test for count data.`binom_test()`

,`pairwise_binom_test()`

,`pairwise_binom_test_against_p()`

and`multinom_test()`

: performs exact binomial and multinomial tests. Alternative to the chi-square test of goodness-of-fit-test when the sample.`counts_to_cases()`

: converts a contingency table or a data frame of counts into a data frame of individual observations.- New functions
`mcnemar_test()`

and`cochran_qtest()`

for comparing two ore more related proportions. `prop_trend_test()`

: Performs chi-squared test for trend in proportion. This test is also known as Cochran-Armitage trend test.

- Now
`get_test_label()`

and`get_pwc_label()`

return expression by default - Unit testing and spelling check added
- Code rewritten to adapt tidyr 1.0.0

`get_anova_table()`

supports now an object of class`grouped_anova_test`

- ANOVA table is now correctly returned when
`correction = "none"`

for repeated measures ANOVA `NAs`

are now automatically removed before quantile computation for identifying outliers (@IndrajeetPatil, #10).- Unquoted factor variable name is now supported in factor manipulation functions:
`set_ref_level()`

,`reorder_levels()`

and`make_valid_levels()`

- New argument
`model`

added in the function`emmeans_test()`

- Adapting to tidyr v1.0.0 (@jennybc, #6)

- New function
`welch_anova_test()`

: Welch one-Way ANOVA test. A wrapper around the base function`stats::oneway.test()`

. This is is an alternative to the standard one-way ANOVA in the situation where the homogeneity of variance assumption is violated. - New function
`friedman_effsize()`

, computes the effect size of Friedman test using the Kendall’s W value. - New function
`friedman_test()`

, provides a pipe-friendly framework to perform a Friedman rank sum test, which is the non-parametric alternative to the one-way repeated measures ANOVA test. - New function
`games_howell_test()`

: Performs Games-Howell test, which is used to compare all possible combinations of group differences when the assumption of homogeneity of variances is violated. - New function
`kruskal_effsize()`

for computing effect size for Kruskal-Wallis test. - New functions added to round and format p-values:
`p_round(), p_format(), p_mark_significant()`

. - New function
`wilcox_effsize()`

added for computing effect size (r) for wilcoxon test. - New function
`get_anova_table()`

added to extract ANOVA table from`anova_test()`

results. Can apply sphericity correction automatically in the case of within-subject (repeated measures) designs. - New functions added to extract information from statistical tests:
`get_anova_label()`

- New function
`emmeans_test()`

added for pairwise comparisons of estimated marginal means.

- the unnecessary column
`comparison`

removed from`tukey_hsd()`

results (breaking change). - New column
`n`

(sample count) added to statistical tests results:`t_test()`

,`wilcox_test()`

,`sign_test()`

,`dunn_test()`

and`kruskal_test()`

(@ShixiangWang, #4). `rstatix_test`

class added to`anova_test()`

results- the results of
`kruskal_test()`

is now an object of class`rstatix_test`

that has an attribute named**args**for holding the test arguments. - In
`get_y_position()`

, y positions and test data are merged now for grouped plots. - New argument
`y.trans`

added in`get_y_position()`

for y scale transformation. - significance column added in
`tukey_hsd()`

results. `adjust_pvalue()`

now supports grouped data

`detailed`

arguments correctly propagated when grouped stats are performed

- New function
`get_pvalue_position`

added to autocompute p-value positions for plotting significance using ggplot2. - New function
`get_comparisons()`

added to create a list of possible pairwise comparisons between groups. - New function
`dunn_test()`

added for multiple pairwise comparisons following Kruskal-Wallis test. - New function
`sign_test()`

added.

`get_summary_stats()`

now supports type = “min”, “max”, “mean” or “median”- the results of
`t_test()`

,`wilcox_test()`

,`dunn_test()`

and`sign_test()`

are now an object of class`rstatix_test`

that has an attribute named**args**for holding the test arguments. - The results of
`cohens_d()`

is now a data frame containing the Cohen’s d and the magnitude.

- the argument
`detatiled`

is now passed to`compare_pairs()`

.

First release