Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson's A, Kendall's W, Cohen's Kappa, ...

Implements a 'Shiny Item Analysis' module and functions for computing false positive rate 
  and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) 
   <doi:10.1111/bmsp.12343>.

Calculates various chance-corrected agreement coefficients (CAC) among 2 or more raters are provided. 
	Among the CAC coefficients covered are Cohen's kappa, Conger's kappa, Fleiss' kappa, Brennan-Prediger coefficient, Gwet's AC1/AC2 
	coefficients, and Krippendorff's alpha. Multiple sets of weights are proposed for computing weighted analyses. All of these statistical 
	procedures are described in details in Gwet, K.L. (2014,ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
	Advanced Analytics, LLC.

Functions to help with analysis of longitudinal data featuring irregular observation times, where the observation times may be associated with the outcome process. There are functions to quantify the degree of irregularity, fit inverse-intensity weighted Generalized Estimating Equations (Lin H, Scharfstein DO, Rosenheck RA (2004) <doi:10.1111/j.1467-9868.2004.b5543.x>), perform multiple outputation (Pullenayegum EM (2016) <doi:10.1002/sim.6829>) and fit semi-parametric joint models (Liang Y (2009) <doi: 10.1111/j.1541-0420.2008.01104.x>).

Calculates various intraclass correlation coefficients used to quantify 
  inter-rater and intra-rater reliability.  The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented
  in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, 
  Advanced Analytics, LLC.

Provides coefficients of interrater reliability that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) \doi{10.1037/1082-989X.1.1.30} and the Coefficient of Concordance according to Kendall & Babington Smith (1939) \doi{10.1214/aoms/1177732186}.