Package: MBRM 0.1.1

Jalmar M. F. Carrasco

MBRM: Mixed Regression Models with Generalized Log-Gamma Random Effects

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).

Authors:Lizandra C. Fabio [aut], Vanessa Barros [aut], Cristian Lobos [aut], Jalmar M. F. Carrasco [aut, cre]

MBRM_0.1.1.tar.gz
MBRM_0.1.1.zip(r-4.7-x86_64)MBRM_0.1.1.zip(r-4.7-arm64)MBRM_0.1.1.zip(r-4.6-x86_64)MBRM_0.1.1.zip(r-4.6-arm64)
MBRM_0.1.1.tar.gz(r-4.7-arm64)MBRM_0.1.1.tar.gz(r-4.7-x86_64)MBRM_0.1.1.tar.gz(r-4.6-arm64)MBRM_0.1.1.tar.gz(r-4.6-x86_64)
MBRM_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
MBRM/json (API)

# Install 'MBRM' in R:
install.packages('MBRM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

cpp

1.00 score 312 downloads 3 exports 27 dependencies

Last updated from:3acf06f332. Checks:10 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK118
linux-devel-x86_64OK127
source / vignettesOK183
linux-release-arm64OK124
linux-release-x86_64OK152
windows-devel-arm64OK211
windows-devel-x86_64OK139
windows-release-arm64OK200
windows-release-x86_64OK101
wasm-releaseOK111

Exports:envelope.MRMMRMfitrMRM

Dependencies:clicpp11dplyrfarverFormulagenericsggplot2gluegtableisobandlabelinglifecyclemagrittrpillarpkgconfigR6RColorBrewerRcpprlangS7scalestibbletidyselectutf8vctrsviridisLitewithr