Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
             and psychometric assessment. EGA estimates the number of dimensions in
	     	 psychological data using network estimation methods and community detection
             algorithms. A bootstrap method is provided to assess the stability of dimensions
	     	 and items. Fit is evaluated using the Entropy Fit family of indices. Unique 
             Variable Analysis evaluates the extent to which items are locally dependent (or
             redundant). Network loadings provide similar information to factor loadings and
	     	 can be used to compute network scores. A bootstrap and permutation approach are
             available to assess configural and metric invariance. Hierarchical structures
             can be detected using Hierarchical EGA. Time series and intensive longitudinal 
	     	 data can be analyzed using Dynamic EGA, supporting individual, group, and 
             population level assessments.

A comprehensive, vectorised toolkit for estimating glomerular
    filtration rate (eGFR) and creatinine clearance from serum creatinine,
    cystatin C, or both. Implements adult, paediatric, and
    neonatal equations, including the Chronic Kidney Disease Epidemiology
    Collaboration (CKD-EPI) equations (2009, 2012, 2021), the Modification
    of Diet in Renal Disease (MDRD) Study equation, Cockcroft-Gault, the
    European Kidney Function Consortium (EKFC) equations, the Full Age
    Spectrum (FAS) equations, the Lund-Malmoe equations, the Berlin
    Initiative Study (BIS) equations, the Schwartz bedside equation, the
    Chronic Kidney Disease in Children Under 25 (CKiD U25) equations, the
    Caucasian, Asian, Paediatric, and Adult (CAPA) cystatin C equation, and
    a neonatal equation. Helpers for body surface area, chronic kidney
    disease (CKD) staging following the Kidney Disease: Improving Global
    Outcomes (KDIGO) guideline, and unit conversions are included.
    Methods are described in Levey et al. (2009) <doi:10.7326/0003-4819-150-9-200905050-00006>,
    Inker et al. (2021) <doi:10.1056/NEJMoa2102953>, and
    Pottel et al. (2021) <doi:10.7326/M20-4366>.
    Inspired by the 'kidney.epi' package.