Functionality for reliability estimates. For 'unidimensional' tests:
    Coefficient alpha, 'Guttman's' lambda-2/-4/-6, the Greatest lower
    bound and coefficient omega_u ('unidimensional') in a Bayesian and a frequentist version. 
    For multidimensional tests: omega_t (total) and omega_h (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_u, 
    is sampling from the posterior inverse 'Wishart' for the 
    covariance matrix based measures (see 'Murphy', 2007, 
    <https://groups.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>. 
    The Bayesian omegas (u, t, and h) are obtained by 
    'Gibbs' sampling from the conditional posterior distributions of 
    (1) the single factor model, (2) the second-order factor model, (3) the bi-factor model, 
    (4) the correlated factor model
    ('Lee', 2007, <doi:10.1002/9780470024737>).

Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022) <doi:10.1111/rssb.12491>.

Provides functionality for determining the sample size of replication studies using Bayesian design approaches in the normal-normal hierarchical model (Pawel et al., 2022) <doi:10.48550/arXiv.2211.02552>.

Contains posterior samplers for the Bayesian piecewise linear log-hazard and piecewise exponential hazard models, including Cox models. Posterior mean restricted survival times are also computed for non-Cox an Cox models with only treatment indicators. The ApproxMean() function can be used to estimate restricted posterior mean survival times given a vector of patient covariates in the Cox model. Functions included to return the posterior mean hazard and survival functions for the piecewise exponential and piecewise linear log-hazard models. Chapple, AG, Peak, T, Hemal, A (2020). Under Revision.