Package: BeQut 0.1.0

Antoine Barbieri

BeQut: Bayesian Estimation for Quantile Regression Mixed Models

Using a Bayesian estimation procedure, this package fits linear quantile regression models such as linear quantile models, linear quantile mixed models, quantile regression joint models for time-to-event and longitudinal data. The estimation procedure is based on the asymmetric Laplace distribution and the 'JAGS' software is used to get posterior samples (Yang, Luo, DeSantis (2019) <doi:10.1177/0962280218784757>).

Authors:Antoine Barbieri [aut, cre], Hélène Jacqmin-Gadda [aut]

BeQut_0.1.0.tar.gz
BeQut_0.1.0.tar.gz(r-4.5-noble)BeQut_0.1.0.tar.gz(r-4.4-noble)
BeQut_0.1.0.tgz(r-4.4-emscripten)BeQut_0.1.0.tgz(r-4.3-emscripten)
BeQut.pdf |BeQut.html✨
BeQut/json (API)
NEWS

# Install 'BeQut' in R:
install.packages('BeQut', repos = 'https://cloud.r-project.org')
Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • 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.

jagscpp

1.70 score 1 stars 248 downloads 4 exports 10 dependencies

Last updated 1 years agofrom:6248e03603. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 26 2025
R-4.5-linuxOKMar 26 2025
R-4.4-linuxOKMar 26 2025

Exports:deviancelqmlqmmqrjm

Dependencies:codajagsUIlatticelqmmMASSMatrixnlmerjagsSparseGridsurvival

Citation

To cite package ‘BeQut’ in publications use:

Barbieri A, Jacqmin-Gadda H (2023). BeQut: Bayesian Estimation for Quantile Regression Mixed Models. R package version 0.1.0, https://CRAN.R-project.org/package=BeQut.

Corresponding BibTeX entry: 

  @Manual{,
    title = {BeQut: Bayesian Estimation for Quantile Regression Mixed
      Models},
    author = {Antoine Barbieri and Hélène Jacqmin-Gadda},
    year = {2023},
    note = {R package version 0.1.0},
    url = {https://CRAN.R-project.org/package=BeQut},
  }

Readme and manuals

BeQut

BeQut is a R-package for Bayesian estimation of quantile regression mixed models. Based on the asymmetric Laplace distribution, it also allows to estimate joint models for longitudinal and time-to-event data, linear mixed effects models and simple linear models.

Yang, M., Luo, S., & DeSantis, S. (2019). Bayesian quantile regression joint models: Inference and dynamic predictions. Statistical Methods in Medical Research, 28(8), 2524–2537. https://doi.org/10.1177/0962280218784757

To try the current development version from github, use:

if (!requireNamespace("devtools", quietly = TRUE)) {
    install.packages("devtools")}
devtools::install_github("AntoineBbi/BeQut")

Warning: BeQut package requires JAGS software (http://mcmc-jags.sourceforge.net/).