htestClust: A Package for Marginal Inference of Clustered Data Under Informative Cluster Size
Mary E. Gregg, Somnath Datta, Douglas J. Lorenz · The R Journal · 2022
When observations are collected in/organized into observational units, within which observations may be dependent, those observational units are often referred to as \"clustered\" and the data as \"clustered data\". Examples of clustered data include repeated measures or hierarchical shared association (e.g., individuals within families). This paper provides an overview of the R package [htestClust](https://CRAN.R-project.org/package=htestClust), a tool for the marginal analysis of such clustered data with potentially informative cluster and/or group sizes. Contained in htestClust are clustered data analogues to the following classical hypothesis tests: rank-sum, signed rank, $t$-, one-way ANOVA, F, Levene, Pearson/Spearman/Kendall correlation, proportion, goodness-of-fit, independence, and McNemar. Additional functions allow users to visualize and test for informative cluster size. This package has an easy-to-use interface mimicking that of classical hypothesis-testing functions in the R environment. Various features of this package are illustrated through simple examples.