Title: Bayesian t Regression for Modeling Mean and Scale Parameters
Author: Margarita Marin and Edilberto Cepeda-Cuervo.
Imports: MASS (>= 7.3), Matrix (>= 1.2), mvtnorm (>= 1.1)
Description: Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.

Author: Julius Pfadt and Don van den Bergh
Description: So far, it provides the most common single test reliability estimates, being:
Coefficient Alpha, Guttman's lambda-2/-4/-6, greatest lower bound and Mcdonald's Omega.
The Bayesian estimates are provided with credible intervals.
The method for the Bayesian estimates, except for omega, is sampling from the posterior inverse Wishart for the covariance matrix based measures.
Gibbs Sampling from the joint conditional distributions of a single factor model in the case of omega.
Methods for the glb are from Moltner and Revelle (2018) <https://www.rdocumentation.org/packages/psych/versions/1.8.10/topics/glb.algebraic>;
the principal factor analysis is from Schlegel (2017) <https://www.r-bloggers.com/iterated-principal-factor-method-of-factor-analysis-with-r/>;
and the analytic alpha interval is from Bonnett and Wright (2014) <doi:10.1002/job.1960>.
Imports: LaplacesDemon, Rcsdp, MASS, ggplot2, ggridges, lavaan,
plotrix, coda, methods, stats, graphics, Rdpack

Author: Julius M. Pfadt and Don van den Bergh
Maintainer: Julius M. Pfadt <julius.pfadt@gmail.com>
Description: Functionality for the most common single test reliability estimates is provided:
Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the greatest lower bound and coefficient omega.
The frequentist estimates are provided with bootstrapped confidence intervals
The method for the Bayesian estimates, except for omega,
is sampling from the posterior inverse 'Wishart' for the covariance matrix based measures.
In the case of omega it is 'Gibbs' Sampling from the joint conditional distributions of a single factor model.
The glb method is adjusted code from the 'Rcsdp' package by 'Hector Corrada Bravo',
the principal factor analysis for the frequentist omega is from 'Schlegel' (2017)
and the analytic alpha interval is from 'Bonett' and 'Wright' (2014) <doi:10.1002/job.1960>.