Bayesian Estimation of the Additive Main Effects and
Multiplicative Interaction Model
c(
person(c("Muhammad", "Yaseen"),  email = "myaseen208@gmail.com", role = c("aut", "cre"))
, person(c("Jose", "Crossa"),  email = "j.crossa@cgiar.org", role = c("aut", "ctb"))
, person(c("Sergio", "Perez-Elizalde"),  email = "sergiop@colpos.mx", role = c("aut", "ctb"))
, person(c("Diego", "Jarquin"),  email = "diego.jarquin@gmail.com", role = c("aut", "ctb"))
, person(c("Jose", "Miguel", "Cotes"),  email = " ", role = c("aut", "ctb"))
, person(c("Kert", "Viele"),  email = " ", role = c("aut", "ctb"))
, person(c("Genzhou", "Liu"),  email = " ", role = c("aut", "ctb"))
, person(c("Paul", "L.", " Cornelius"),  email = " ", role = c("aut", "ctb"))
)
Performs Bayesian estimation of the additive main effects and multiplicative interaction (AMMI) model. The method is explained in Crossa, J., Perez-Elizalde, S., Jarquin, D., Cotes, J.M., Viele, K., Liu, G. and Cornelius, P.L. (2011) (<doi:10.2135/cropsci2010.06.0343>).
dplyr, ggplot2, lme4, magrittr, MASS, mvtnorm, rlang,
rstiefel, scales, stats, tibble, tidyr, tmvtnorm
Muhammad Yaseen [aut, cre],
Jose Crossa [aut, ctb],
Sergio Perez-Elizalde [aut, ctb],
Diego Jarquin [aut, ctb],
Jose Miguel Cotes [aut, ctb],
Kert Viele [aut, ctb],
Genzhou Liu [aut, ctb],
Paul L.  Cornelius [aut, ctb]

Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.

Bayesian Inference in the Analysis of Variance via Markov Chain
Monte Carlo in Gaussian Mixture Models

c(
person("Jane", "Pan", email = "jpan1@ucla.edu", role = c("cre", "aut")),
person("Sudipto", "Banerjee", email = "sudipto@ucla.edu", role = "aut"))
Computes Bayesian assurance under various
settings characterized by different assumptions and objectives, including
precision-based conditions, credible intervals, and goal functions.
All simulation-based functions included in this package rely on a two-stage
Bayesian method that assigns two distinct priors to evaluate the
probability of observing a positive outcome, which addresses subtle
limitations that take place when using the standard single-prior approach.
For more information, please refer to Pan and Banerjee (2021)
<arXiv:2112.03509>.
Jane Pan [cre, aut],
Sudipto Banerjee [aut]