The code in R and guide document were prepared by Margarita Marin  and Javier Rojas and Daniel Jaimes, under the direction of the professor Edilberto Cepeda and with collaboration of Martha Corrales, Maria Fernanda Zarate, Ricardo Duplat and Luis F Villarraga.

Bayesian Beta regression: joint mean and precision modeling
The code in R and guide document were prepared by Margarita Marin  and Javier Rojas and Daniel Jaimes, under the direction of Edilberto Cepeda-Cuervo and with collaboration of Martha Corrales, Maria Fernanda Zarate, Ricardo Duplat and Luis F Villarraga.

The code in R and guide document were prepared by Margarita Marin,
Javier Rojas, Daniel Jaimes and Hugo Andres Gutierrez Rojas, under the
direction of professor Edilberto Cepeda-Cuervo and with collaboration of
Martha Corrales, Maria Fernanda Zarate, Ricardo Duplat and Luis F
Villarraga.

Bayesian Gamma Regression: Joint Mean and Shape Modeling
c(person("Arturo", "Camargo Lozano" , email ="bacamargol@unal.edu.co", role = c("aut", "cre")),
person("Edilberto", "Cepeda Cuervo", email ="ecepedac@unal.edu.co", role = "aut")      )
Arturo Camargo Lozano [aut, cre],
Edilberto Cepeda Cuervo [aut]
Adjust the Gamma regression models from a Bayesian perspective described by Cepeda and Urdinola (2012) <doi:10.1080/03610918.2011.600500>, modeling the parameters of mean and shape and using different link functions for the parameter associated to the mean. And calculates different adjustment statistics such as the Akaike information criterion and Bayesian information criterion.

Bayesian t Regression for Modeling Mean and Scale Parameters
Margarita Marin and Edilberto Cepeda-Cuervo.
Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.

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.
See Murphy (2007) <https://www.seas.harvard.edu/courses/cs281/papers/murphy-2007.pdf>.
Gibbs Sampling from the joint conditional distributions of a single factor model in the case of omega.
See Lee (2007, ISBN:978-0-470-02424-9).
Methods for the glb are from Moltner and Revelle (2018) <https://www.rdocumentation.org/packages/psych/versions/1.8.10/topics/glb.algebraic>;
lambda-4 is from Benton (2015) <doi:10.1007/978-3-319-07503-7_19>;
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>.
LaplacesDemon, Rcsdp, MASS, ggplot2, ggridges, lavaan,
plotrix, coda, methods, stats, graphics, Rdpack