Provides methods for testing the equality of dependent intraclass correlation coefficients (ICCs) estimated using linear mixed-effects models. Several of the implemented approaches are based on the work of Donner and Zou (2002) <doi:10.1111/1467-9884.00324>.

Estimates the intraclass correlation coefficient (ICC) for count data to assess repeatability (intra-methods concordance) and concordance (between-method concordance). In the concordance setting, the ICC is equivalent to the concordance correlation coefficient estimated by variance components. The ICC is estimated using the estimates from generalized linear mixed models. The within-subjects distributions considered are: Poisson; Negative Binomial with additive and proportional extradispersion; Zero-Inflated Poisson; and Zero-Inflated Negative Binomial with additive and proportional extradispersion. The statistical methodology used to estimate the ICC with count data can be found in Carrasco (2010) <doi:10.1111/j.1541-0420.2009.01335.x>.

The functions compute the double-entry intraclass correlation, which is an index of profile similarity (Furr, 2010; McCrae, 2008). The double-entry intraclass correlation is a more precise index of the agreement of two empirically observed profiles than the often-used intraclass correlation (McCrae, 2008). Profiles comprising correlations are automatically transformed according to the Fisher z-transformation before the double-entry intraclass correlation is calculated. If the profiles comprise scores such as sum scores from various personality scales, it is recommended to standardize each individual score prior to computation of the double-entry intraclass correlation (McCrae, 2008). See Furr (2010) <doi:10.1080/00223890903379134> or McCrae (2008) <doi:10.1080/00223890701845104> for details.

A comprehensive toolkit for intraclass correlation coefficient (ICC) analysis,
    integrating three core functionalities: (1) Closed-form sample size calculation for ICC
    estimation with assurance probability, based on Zou (2012) <doi:10.1002/sim.5466>;
    (2) Full implementation of all 10 ICC types (6 common + 4 supplementary) for point
    estimation, exact confidence interval calculation, and formal hypothesis testing,
    following the methods of McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and
    the standard decision framework; (3) An interactive 'shiny' application that guides users
    through ICC type selection, performs calculations, and provides reliability evaluation
    based on the Koo & Li (2016) <doi:10.1016/j.jcm.2016.02.012> criteria.
    Compared to existing packages, it provides a unified decision workflow and supports
    all less common ICC variants.