Package: Romeb 0.1.2

Dandan Tang

Romeb: Robust Median-Based Bayesian Growth Curve Modeling

Implements robust median-based Bayesian growth curve models that handle Missing Completely at Random (MCAR), Missing At Random (MAR), and Missing Not At Random (MNAR) missing-data mechanisms, and allow auxiliary variables. Models are fitted via 'rjags' (interface to 'JAGS') and summarized with 'coda'.

Authors:Dandan Tang [aut, cre], Xin Tong [aut]

Romeb_0.1.2.tar.gz
Romeb_0.1.2.tar.gz(r-4.7-any)Romeb_0.1.2.tar.gz(r-4.6-any)
Romeb_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
Romeb/json (API)
NEWS

# Install 'Romeb' in R:
install.packages('Romeb', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dandantang0/romeb/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:
  • NYS - Youth Attitudes toward Deviance

On CRAN:

Conda:

jagscpp

2.00 score 4 scripts 120 downloads 4 exports 3 dependencies

Last updated from:74f4694880. Checks:4 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK127
source / vignettesOK300
linux-release-x86_64OK109
wasm-releaseOK108

Exports:modelmodel_MNARmodel_MNAR_kRomeb

Dependencies:codalatticerjags

Romeb Package: An Introduction

Dandan Tang, Xin Tong

Rendered fromromeb-intro.Rmdusingknitr::rmarkdownon Jun 15 2026.

Last update: 2025-11-17
Started: 2025-11-17