autotestR: autotestR

In autotestR: Automated Functions for Basic Statistical Tests

autotestR

Description

designed to simplify the execution of the main statistical tests commonly used in the life sciences. It provides user-friendly functions that automatically generate plots and clear explanations, making statistical analysis more accessible for researchers and students.

Main features

• t test (independent and paired)

• Mann–Whitney test (Wilcoxon rank-sum)

• Multiple group comparison (t test or Mann–Whitney)

• Chi-squared test and Fisher’s exact test

• One-way ANOVA with Tukey HSD post hoc test

• Kruskal–Wallis test with Dunn post hoc test

• Pearson, Spearman, and Kendall correlation tests with automatic plots

• Diagnostic function that suggests the most appropriate statistical test

• Intuitive plots fully integrated into the functions

Basic usage

library(autotestR)

Independent t test

group1 <- rnorm(30, 10, 2) group2 <- rnorm(30, 12, 2) test.t(group1, group2)

Chi-squared test

var1 <- sample(c("A", "B"), 100, replace = TRUE) var2 <- sample(c("Yes", "No"), 100, replace = TRUE) test.chi(var1, var2)

Multiple test (t test or Mann–Whitney)

df <- data.frame( control = rnorm(30, 10), treatment = rnorm(30, 12), test1 = rnorm(30, 11), test2 = rnorm(30, 15) ) test.tmulti(df)

ANOVA with post hoc test

g1 <- rnorm(20, 5) g2 <- rnorm(20, 7) g3 <- rnorm(20, 6) test.anova(g1, g2, g3)

Correlation test

x <- rnorm(30) y <- x + rnorm(30, 0, 1) test.correlation(x, y)

Author(s)

Maintainer: Luiz Garcia [e-mail] (ORCID)

autotestR documentation built on April 29, 2026, 1:09 a.m.