Title:	Automated Functions for Basic Statistical Tests
Version:	1.2.15
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:	2026-04-24 01:37:19 UTC; Seu Computador
Author:	Luiz Garcia [aut, cre]
Repository:	CRAN
Date/Publication:	2026-04-28 18:30:16 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.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 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"),
  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,
  method = "auto",
  main = NULL,
  xlab = NULL,
  ylab = NULL,
  style = c("simple", "inference", "structure", "density", "distribution"),
  plot_normality = FALSE,
  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 r² 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 = "Proportion",
  style = c("stacked", "barplot", "mosaic", "pie"),
  show_table = TRUE,
  help = FALSE,
  verbose = TRUE
)

Arguments

Value

Invisible object containing the Fisher test result.

Examples

data <- data.frame(control = c("healthy","healthy","sick","sick","sick"),
                   treatment = c("healthy","healthy","healthy","healthy","sick"))
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(
  x,
  y,
  by,
  title = NULL,
  xlab = NULL,
  ylab = NULL,
  plot = TRUE,
  style = c("clean", "CI", "facet"),
  conf.level = 0.95,
  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(123)

n <- 60

marker <- rnorm(n, 10, 2)
group  <- rep(c("Control", "Treatment"), each = n/2)

# Same intercept, different slopes
response <- 2 +
  ifelse(group == "Control", 0.5, 1.2) * marker +
  rnorm(n, 0, 1)

test.interaction(marker, response, 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"),
  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 means between two independent groups, with automatic checks for normality and homogeneity of variances. If assumptions are violated, the Mann-Whitney test is automatically applied (without generating a plot).

Usage

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

Arguments

Value

Invisible list with summary, test test_result, method and (optionally) plot.

Examples

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

Multiple pairwise comparisons with diagnostics

Description

Performs multiple pairwise comparisons using Student's t-test or Mann-Whitney test, with automatic diagnostics, effect sizes, 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"),
  help = FALSE,
  verbose = TRUE
)

Arguments

Details

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

Effect sizes:

• Cohen's d for t-tests

• Rank-biserial correlation for Mann-Whitney

Value

A list with:

results

A tibble with test results.

plot

A ggplot object.

data_long

Long-format data used for plotting.

Examples

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

test.tmulti(
  df,
  comparisons = list(
    c("control", "treatment"),
    c("treatment", "test1")
  )
)

Paired t-test with advanced visualizations

Description

Performs a paired t-test between two numeric vectors (e.g., before vs after) or between two numeric columns of a data frame. Includes four visualization styles (boxplot, violin, mono, and half-eye).

Usage

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

Arguments

Value

An invisible list containing:

summary_table

Group means and standard deviations

test_result

t-test result object (stats::t.test)

data

Data frame used for plotting

plot

ggplot2 object

Examples

before <- c(13, 12, 15, 14)
after  <- c(9, 11, 10, 10)
test.tpaired(before, after)

Mann-Whitney U Test

Description

Performs the Mann-Whitney (Wilcoxon rank-sum) test for comparing two independent groups, with statistical summary and graphical visualization.

Usage

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

Arguments

Value

Invisible list with:

summary

Group-wise statistical summary

test

Test result (htest object)

plot

ggplot2 visualization object

Examples

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