Collection of Convenient Functions for Common Statistical Computations
Measures of association for contingency tables
Find prime numbers
Kruskal-Wallis test
Mann-Whitney test
Wilcoxon rank sum test
Effect size statistics for anova
Create default priors for brms-models
Standard error and confidence intervals for bootstrapped estimates
Generate nonparametric bootstrap replications
Chi-Squared test
Compute model quality
Test and training error from model cross-validation
Compute model quality
Design effects for two-level mixed models
Determining distribution parameters
Gini's Mean Difference
Compute trends in status inequalities
Proportions of values in a vector
Deprecated functions
Objects exported from other packages
Sample size for linear mixed models
Standard error of sample mean for mixed models
Survey-weighted negative binomial generalised linear model
Survey-weighted zero-inflated Poisson model
Student's t test
Expected and relative table values
Calculate population variance and standard deviation
Weight a variable
Weighted statistics for variables
Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), or for which currently no functions available. Second, another focus lies on weighted variants of common statistical measures and tests like weighted standard error, mean, t-test, correlation, and more.