Authors@R: c(person("Julius M.", "Pfadt", email = "julius.pfadt@gmail.com",
Description: Functionality for the most common single test reliability estimates:
Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower bound and coefficient omega.
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 uses adjusted code from the 'Rcsdp' package by 'Hector Corrada Bravo',
<https://CRAN.R-project.org/package=Rcsdp>. This process applies a slightly adjusted solving algorithm
from the 'CSDP' library by 'Brian Borchers' <https://github.com/coin-or/Csdp/wiki>,
<doi.org/10.1080/10556789908805765>, but is wrapped in 'RcppArmadillo'.
The principal factor analysis for a version of frequentist omega is from 'Schlegel' (2017)
The analytic confidence interval of alpha is from 'Bonett' and 'Wright' (2015) <doi:10.1002/job.1960>.
Imports: LaplacesDemon, MASS, lavaan, coda, methods, stats, graphics,

<https://CRAN.R-project.org/package=Rcsdp>, which now uses a slightly adjusted solving algorithm
from the 'CSDP' library by 'Brian Borchers' <https://github.com/coin-or/Csdp/wiki>, <doi.org/10.1080/10556789908805765>
and the analytic alpha interval is from 'Bonett' and 'Wright' (2015) <doi:10.1002/job.1960>.
Imports: LaplacesDemon, Rcsdp, MASS, lavaan, coda, methods, stats,

Description: Functionality for reliability estimates. For unidimensional tests:
Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
bound and coefficient omega in a Bayesian and a frequentist version.
For multidimensional tests: omega-total and omega-hierarchical.
The results include confidence and credible intervals, the
probability of a coefficient being larger than a cutoff,
and a check for the factor models, necessary for the omega coefficients.
The method for the Bayesian unidimensional estimates, except for omega,
is sampling from the posterior inverse 'Wishart' for the
covariance matrix based measures (see 'Murphy', 2007,
The Bayesian omegas (unidimensional, hierarchical, and total) are obtained by
'Gibbs' Sampling from the conditional posterior distributions of
(1) the single factor model and (2) the higher order factor model