A Nonparametric Trend Test for Independent and Dependent Samples

Implements the nonparametric trend test for one or several samples as proposed by Bathke (2009) . The method provides a unified framework for analyzing trends in both independent and dependent data samples, making it a versatile tool for various study designs. The package allows for the evaluation of different trend alternatives, including two-sided (general trend), monotonic increasing, and monotonic decreasing trends. As a nonparametric procedure, it does not require the assumption of data normality, offering a robust alternative to parametric tests.

📈 nonparTrendR

nonparTrendR is an R package implementing a nonparametric trend test for independent and dependent samples, based on Bathke (2009). It detects consistent monotonic trends (increasing or decreasing) across time points or ordered groups, while accounting for within-subject correlations in repeated measures.

✨ Features

• 📌 Supports both independent and repeated measures designs.
• 📊 Rank-based test statistic (ν̂) with two-sided and directional alternatives.
• 🔗 Handles within-subject dependencies in longitudinal data.
• ✅ Returns standard htest objects for easy integration.

📚 Reference

Bathke, A. C. (2009).
A unified approach to nonparametric trend tests for dependent and independent samples.
Metrika, 69(1), 17–29.

🛠 Installation

# Install from CRAN (after release)
install.packages("nonparTrendR")

# Or install development version from GitHub
devtools::install_github("yourusername/nonparTrendR")

🚀 Quick Example

Independent samples

library(nonparTrendR)

data_indep <- list(
  c(6.62, 6.65, 5.78),  # Group 1
  c(6.25, 6.95, 5.61),  # Group 2
  c(7.11, 5.68, 6.23)   # Group 3
)

nonparTrendR_test(data_indep, type = "I", alternative = "increasing")

Dependent samples

data_dep <- matrix(c(
  8, 6, 5, 5, 4,
  7, 6, 6, 6, 6,
  6, 5, 5, 4, 2
), nrow = 3, byrow = TRUE)

nonparTrendR_test(data_dep, type = "D", alternative = "decreasing")

🧪 Example Use Cases

• Clinical trials (longitudinal symptom scores)
• Industrial process monitoring
• Customer metrics over time
• Seasonal survey responses