Package: MBRM
Type: Package
Title: Mixed Regression Models with Generalized Log-Gamma Random
        Effects
Version: 0.1.1
Authors@R: c(person("Lizandra C.", "Fabio", email = "lizandra.fabio@ufba.br", role = "aut"),
  person("Vanessa", "Barros", email = "vbarrosoliveira@gmail.com", role = "aut"),
  person("Cristian", "Lobos", email = "clobos@usp.br ", role = "aut"),
  person("Jalmar M. F.", "Carrasco", email = "carrasco.jalmar@ufba.br", role = c("aut", "cre")))
Author: Lizandra C. Fabio [aut],
  Vanessa Barros [aut],
  Cristian Lobos [aut],
  Jalmar M. F. Carrasco [aut, cre]
Maintainer: Jalmar M. F. Carrasco <carrasco.jalmar@ufba.br>
Description: Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
LinkingTo: Rcpp
Imports: Rcpp, stats, Formula, tibble, dplyr, ggplot2
Depends: R (>= 3.5)
NeedsCompilation: yes
Packaged: 2025-12-18 12:42:04 UTC; carra
Repository: CRAN
Date/Publication: 2025-12-22 19:50:07 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-29 04:42:04 UTC; unix
Archs: MBRM.so.dSYM
