Package {autotestR}

Title:	Automated Functions for Basic Statistical Tests
Version:	1.3.0
Description:	Provides simple and intuitive functions for basic statistical analyses. Methods include the t-test (Student 1908 <doi:10.1093/biomet/6.1.1>), the Mann-Whitney U test (Mann and Whitney 1947 <doi:10.1214/aoms/1177730491>), Pearson's correlation (Pearson 1895 <doi:10.1098/rspl.1895.0041>), and analysis of variance (Fisher 1925, <doi:10.1007/978-1-4612-4380-9_5>). Functions are compatible with 'ggplot2' and 'dplyr'.
License:	MIT + file LICENSE
Depends:	R (≥ 4.1.0)
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	car, crayon, dplyr, FSA, ggplot2, ggdist, ggExtra, nortest, purrr, RColorBrewer, scales, stats, tibble, tidyr, tidyselect, utils, vcd
NeedsCompilation:	no
Maintainer:	Luiz Garcia <luiz.cardoso@ufpr.br>
Packaged:	2026-10-26 17:27:38 UTC; Seu Computador
Author:	Luiz Garcia [aut, cre]
Repository:	CRAN
Date/Publication:	2026-10-26 18:00:08 UTC

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_t_multi(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 luiz.cardoso@ufpr.br (ORCID)

pre_test — Automatic statistical test suggestion

Description

Automatically identifies whether the input data are numeric or categorical and suggests the most appropriate statistical test.

Usage

pre_test(..., alpha = 0.05, help = FALSE, verbose = TRUE)

Arguments

Value

Invisible list with normality results, homogeneity or contingency table, and test recommendation

ANOVA test with automated assumption checking

Description

Performs ANOVA (and Tukey HSD) if data meet normality and homogeneity assumptions. Otherwise, automatically recommends Kruskal-Wallis/Dunn.

Usage

test_anova(
  ...,
  title = "ANOVA/Tukey HSD",
  xlab = "Group",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

An aov object or a recommendation message.

Examples

df <- data.frame(
  control = rnorm(30, 10),
  treatment = rnorm(30, 12),
  test = rnorm(30, 11)
)
test_anova(df)

Chi-square test

Description

Applies the Pearson chi-square test or Fisher's exact test to assess association between two categorical variables.

Usage

test_chi(
  x,
  y = NULL,
  title = "Chi-square Test",
  xlab = NULL,
  ylab = "Proportion",
  style = c("stacked", "barplot", "mosaic", "pie"),
  colors = NULL,
  show_table = TRUE,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

Test result and contingency table.

Examples

data <- data.frame(
  control = c(rep("healthy", 50), rep("sick", 150)),
  treatment = c(rep("healthy", 100), rep("sick", 100))
)
test_chi(data)

Correlation Analysis with Automatic Method Selection

Description

Performs correlation analysis between two numeric variables using Pearson, Spearman, or Kendall methods. When method = "auto", the function automatically selects the most appropriate method based on normality, presence of ties, and proportion of outliers.

Usage

test_correlation(
  x,
  y = NULL,
  style = c("simple", "inference", "structure", "density", "distribution"),
  method = "auto",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  plot_normality = FALSE,
  line_col = NULL,
  point_col = NULL,
  ci_fill = NULL,
  help = FALSE,
  verbose = TRUE
)

## S3 method for class 'test_correlation'
print(x, ...)

Arguments

Details

In addition to hypothesis testing, the function provides diagnostic information, effect size interpretation, and publication-ready visualizations.

Method selection in automatic mode follows these rules:

• If more than 5\ Kendall's tau is used.

• If both variables are normally distributed and no ties are present, Pearson's correlation is used.

• Otherwise, Spearman's rank correlation is applied.

Normality is assessed using Shapiro-Wilk (n <= 50), Anderson-Darling (50 < n <= 300), or Kolmogorov-Smirnov (n > 300) tests.

Value

An object of class "test_correlation" containing:

• call: Matched function call.

• data: Input data (if n <= 5000).

• n: Sample size.

• method: Selected correlation method.

• estimate: Correlation coefficient.

• effect: Effect size information (r, rho, or tau, and r^2 when applicable).

• conf.int: Confidence interval.

• p.value: P-value of the test.

• interpretation: Qualitative interpretation of effect size.

• diagnostics: Normality, ties, and outlier diagnostics.

• decision: Automatic selection rationale.

• htest: Underlying stats::cor.test object.

• plot: ggplot2 object.

See Also

cor.test, ggplot

Examples

# Pearson (approximately normal data)
set.seed(123)
x <- rnorm(50, sd = 0.1)
y <- x + rnorm(50, sd = 0.1)
test_correlation(x, y)

# Spearman (non-normal data)
set.seed(123)
x <- runif(300)
y <- log(x + 0.1) + rnorm(300, sd = 0.5)
test_correlation(x, y)

# Kendall (ties and outliers)
set.seed(123)
x <- runif(1000, 1, 100)
y <- sin(x) * 30 + rnorm(1000, 0, 10)
x[sample(1:500, 50)] <- 50
y[sample(1:500, 50)] <- 0
x_out <- runif(100, 10, 20)
y_out <- runif(100, 80, 120)
x <- c(x, x_out)
y <- c(y, y_out)
test_correlation(x, y)

Fisher's Exact Test

Description

Performs Fisher's Exact Test using two categorical vectors or a data frame with two columns, constructing a contingency table and optionally generating graphical visualizations.

Usage

test_fisher(
  x,
  y = NULL,
  title = "Fisher's Exact Test",
  xlab = NULL,
  ylab = NULL,
  style = c("stacked", "barplot", "mosaic", "pie"),
  colors = NULL,
  show_table = TRUE,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

Invisible object containing the Fisher test result.

Examples

data <- data.frame(control = c(rep("healthy", 1), rep("sick", 5)),
                   treatment = c(rep("healthy", 5), rep("sick", 1)))
test_fisher(data)

Test for interaction between a continuous predictor and a grouping variable

Description

Fits a linear model of the form y ~ x * by to evaluate whether the association between a continuous predictor and an outcome differs across groups. Optionally produces a publication-ready visualization of group-specific regression lines.

Usage

test_interaction(
  y,
  x,
  by,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  plot = TRUE,
  style = c("clean", "CI", "facet"),
  conf.level = 0.95,
  colors = NULL,
  verbose = TRUE,
  help = FALSE
)

## S3 method for class 'test_interaction'
print(x, ...)

Arguments

Details

The interaction coefficient (\beta) represents the difference in regression slopes between groups, conditional on the reference level of by. The sign and magnitude of this coefficient depend on the chosen reference group.

Confidence intervals are emphasized as the primary inferential quantity.

Value

An object of class "test_interaction" containing:

• model: the fitted linear model,

• interaction: estimated interaction effects with confidence intervals,

• plot: a ggplot object (if plot = TRUE).

Examples

# Simple example: different trends between groups

set.seed(125)

n <- 60
df_bio <- data.frame(
  response = rnorm(n, mean = 10, sd = 2),
  group    = rep(c('Control', 'Treatment'), each = n / 2)
)

df_bio$marker <- with(
  df_bio,
  ifelse(
    group == 'Control',
    2 + 1.3 * response + rnorm(n, 0, 2),
    2 + 1.2 * response + rnorm(n, 0, 1)
  )
)

test_interaction(
  x  = df_bio$marker,
  y  = df_bio$response,
  by = df_bio$group)

Kruskal-Wallis Test with Dunn Post-hoc

Description

Performs the Kruskal-Wallis rank-sum test for comparing three or more independent groups, followed by Dunn's post-hoc test with multiple comparison adjustment.

Usage

test_kruskal(
  ...,
  title = "Kruskal-Wallis + Dunn",
  xlab = "Group",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  adjust = c("bonferroni", "holm", "bh"),
  colors = NULL,
  help = FALSE,
  verbose = TRUE
)

Arguments

Details

This function is a non-parametric alternative to one-way ANOVA and is recommended when normality or homoscedasticity assumptions are violated.

Value

Invisibly returns a list with the following components:

type

Test type.

H

Kruskal-Wallis H statistic.

df

Degrees of freedom.

p

Global test p-value.

epsilon_sq

Epsilon-squared effect size.

epsilon_ci

Bootstrap confidence interval for effect size.

means_sd

Group means and standard deviations.

dunn

Dunn post-hoc results.

significant_pairs

Significant pairwise comparisons.

data

Long-format data used in the analysis.

Examples

set.seed(123)

n <- 25

df <- data.frame(
  control    = rexp(n, rate = 1),
  treatment1 = rexp(n, rate = 0.6),
  treatment2 = rgamma(n, shape = 2, scale = 1)
)

test_kruskal(df)

Student's t-test (with automatic diagnostics)

Description

Performs Student's t-test to compare the means of two independent groups, with automatic checks for normality and homogeneity of variances. If the assumptions are violated, the Mann-Whitney U test is applied automatically, without generating a plot.

Usage

test_t(
  ...,
  title = TRUE,
  title_text = "t-test",
  xlab = "Group",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  colors = NULL,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

An object of class test_t containing the following components:

• summary: Summary statistics, including group means, standard deviations (SD), and group information.

• result: Test results, including the test statistic, degrees of freedom (DF), and p-value.

• method: The statistical method selected for the analysis.

• normality: Results of the normality assessment.

• homogeneity: Results of the homogeneity of variances assessment.

• plot: Plot parameters and related information.

Examples

set.seed(123)
df <- data.frame(
  control = rnorm(30, mean = 10),
  treatment = rnorm(30, mean = 15)
)
test_t(df)

Paired t-test with advanced visualizations

Description

Performs a paired t-test to compare the means of two related measurements, such as before-and-after observations, using two numeric vectors of equal length or two numeric columns from a data frame. Includes four visualization styles: boxplot, violin, mono, and halfeye.

Usage

test_t_paired(
  ...,
  title = "Paired t-test",
  xlab = "",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  colors = NULL,
  connect = TRUE,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

An invisible list containing the following components:

summary_table

Summary statistics, including group means and standard deviations.

test_result

Result object returned by stats::t.test, containing the paired t-test results.

data

Data frame containing the data used for plotting.

plot

Plot object generated using ggplot2.

Examples

before <- c(13, 12, 15, 14)
after <- c(9, 11, 10, 10)
test_t_paired(before, after)

Multiple pairwise comparisons with diagnostics

Description

Performs multiple pairwise comparisons using Student's t-test or the Mann-Whitney U test, with automatic diagnostic checks, effect size estimation, confidence intervals, multiple testing correction, and visualization.

Usage

test_tmulti(
  ...,
  comparisons = NULL,
  title = "Multiple comparisons (t / MW)",
  xlab = "",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  p_adjust = c("none", "holm", "BH", "bonferroni"),
  colors = NULL,
  help = FALSE,
  verbose = TRUE
)

Arguments

Details

Normality is assessed using the Shapiro-Wilk test, and homogeneity of variances is assessed using Levene's test. When the assumptions are met, a pooled-variance Student's t-test is used. Otherwise, the Mann-Whitney U test is applied with bootstrap confidence intervals.

Effect sizes are calculated as follows:

• Cohen's d for t-tests.

• Rank-biserial correlation for Mann-Whitney U tests.

Value

A list containing the following components:

results

A tibble containing the pairwise test results.

plot

A plot object generated using ggplot2.

data_long

Long-format data used for plotting.

Examples

set.seed(123)
df <- data.frame(
  control = rnorm(30, mean = 10),
  treatment = rnorm(30, mean = 12),
  test1 = rnorm(30, mean = 11),
  test2 = rnorm(30, mean = 15)
)

test_tmulti(
  df,
  comparisons = list(
    c("control", "treatment"),
    c("treatment", "test1")
  )
)

Mann-Whitney U Test

Description

Performs the Mann-Whitney U test (Wilcoxon rank-sum test) to compare two independent groups, providing a statistical summary and graphical visualization.

Usage

test_u(
  ...,
  title = "Mann-Whitney Test",
  xlab = "Group",
  ylab = "Value",
  style = c("boxplot", "violin", "mono", "halfeye"),
  colors = NULL,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

An invisible list containing the following components:

summary

Group-wise descriptive statistics.

test

An object of class htest containing the Mann-Whitney U test results.

plot

A plot object generated using ggplot2.

Examples

x <- c(1, 3, 5, 6)
y <- c(7, 8, 9, 12)
data <- data.frame(groupA = x, groupB = y)
test_u(data)