A comprehensive R package for intraclass correlation coefficient (ICC) analysis and sample size planning.

Features

Full support for all 10 ICC types from McGraw & Wong (1996)
Intuitive 4-question decision framework (no need to memorize ICC codes)
Automated reliability evaluation based on Koo & Li (2016)
Publication-ready report generation
Sample size and power analysis
Interactive Shiny web application with visualization
Comprehensive data validation and error handling

Installation

# Install devtools if not already installed
install.packages("devtools")
# Install ICCDesign from GitHub
devtools::install_github("KlariZhang/ICCDesign")
# Load the package
library(ICCDesign)

Quick Start

Command Line Interface

# Use the built-in example dataset
data(icc_data)
# Calculate ICC (two-way random, single rating, absolute agreement)
result <- icc_calc(
  data = icc_data,
  same_raters = TRUE,
  rater_effect = "random",
  rating_type = "single",
  agreement_type = "absolute"
)
# View the full report
print(result)
# Extract specific results
result$icc_result$point_est  # ICC point estimate
result$evaluation$rating_en  # Reliability rating

Interactive Shiny Interface

### Launch the point-and-click web application
run_icc_app() 3. Sample Size Calculation

Sample Size Calculation

### Calculate required sample size for 80% assurance that ICC >= 0.75
n <- icc_sample_size(
method = "lower",
rho = 0.85,
rating_target = "good",
k = 3,
same_raters = TRUE,
rater_effect = "random",
rating_type = "single",
agreement_type = "absolute"
)
cat("Required sample size:", n, "\n")

References

McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological Methods, 1(1), 30-46.
Koo, T. K., & Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of Chiropractic Medicine, 15(2), 155-163.