Pipe-Friendly Framework for Basic Statistical Tests
Add P-value Significance Symbols
Adjust P-values for Multiple Comparisons
Create Nice Summary Tables of ANOVA Results
Anova Test
Convert a Correlation Test Data Frame into a Correlation Matrix
Exact Binomial Test
Box's M-test for Homogeneity of Covariance Matrices
Chi-squared Test for Count Data
Cochran's Q Test
Compute Cohen's d Measure of Effect Size
Replace Correlation Coefficients by Symbols
Add Significance Levels To a Correlation Matrix
Compute Correlation Matrix with P-values
Visualize Correlation Matrix Using Base Plot
Reorder Correlation Matrix
Reshape Correlation Data
Subset Correlation Matrix
Correlation Test
Convert a Table of Counts into a Data Frame of cases
Compute Cramer's V
Arrange Rows by Column Values
Get User Specified Variable Names
Group a Data Frame by One or more Variables
Functions to Label Data Frames by Grouping Variables
Nest a Tibble By Groups
Select Columns in a Data Frame
Split a Data Frame into Subset
Unite Multiple Columns into One
Alternative to dplyr::do for Doing Anything
Dunn's Test of Multiple Comparisons
Pairwise Comparisons of Estimated Marginal Means
Effect Size for ANOVA
Build Factorial Designs for ANOVA
Factors
Fisher's Exact Test for Count Data
Compute Frequency Table
Friedman Test Effect Size (Kendall's W Value)
Friedman Rank Sum Test
Games Howell Post-hoc Tests
Create a List of Possible Comparisons Between Groups
Compute Mode
Autocompute P-value Positions For Plotting Significance
Compute Summary Statistics
Extract Label Information from Statistical Tests
Kruskal-Wallis Effect Size
Kruskal-Wallis Test
Levene's Test
Compute Mahalanobis Distance and Flag Multivariate Outliers
Make Clean Names
Manova exported from car package
McNemar's Chi-squared Test for Count Data
Exact Multinomial Test
Identify Univariate Outliers Using Boxplot Methods
Rounding and Formatting p-values
Pipe operator
Proportion Test
Test for Trend in Proportions
Pull Lower and Upper Triangular Part of a Matrix
Objects exported from other packages
Remove Non-Significant from Statistical Tests
Replace Lower and Upper Triangular Part of a Matrix
Sample n Rows By Group From a Table
Shapiro-Wilk Normality Test
Sign Test
T-test
Tukey Honest Significant Differences
Welch One-Way ANOVA Test
Wilcoxon Effect Size
Wilcoxon 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.