Skip to content
R tools for a Bayesian approach to estimating Generalized Graded Unfolding Model (GGUM) (Roberts, Donoghue, and Laughlin 2000) parameters
R C++
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Type Name Latest commit message Commit time
Failed to load latest commit information.
R Fix minor typos in docs Jan 8, 2020
data-raw Complete vignette Dec 15, 2019
man Update CRAN comments and fix documentation typo Jan 18, 2020
tests Prepare for CRAN resubmission Jan 11, 2020
vignettes Fix minor typos in docs Jan 8, 2020
.Rbuildignore Increment version and update README [ci skip] Jan 19, 2020
.gitignore Complete vignette Dec 15, 2019
.travis.yml Fix typos in documentation and .travis.yml Dec 19, 2019
DESCRIPTION Increment version and update README [ci skip] Jan 19, 2020
NAMESPACE Update color palettes Dec 16, 2019 Increment version and update README [ci skip] Jan 19, 2020
appveyor.yml Update appveyor.yml Feb 4, 2019
codecov.yml Add more tests, setup Codecov, eliminate cruft May 14, 2019 Update CRAN comments [ci skip] Jan 18, 2020


Travis-CI Build Status AppVeyor Build Status Coverage status CRAN_Status_Badge CRAN_Downloads_Badge License: GPL v2

bggum provides an implementation for the Generalized Graded Unfolding Model (GGUM) of Roberts, Donoghue, and Laughlin (2000) for R. Rather than provide tools for marginal maximum likelihood estimation, which is available from the package GGUM currently available on CRAN, we provide an implementation of the MCMC algorithm in de la Torre et al (2006) as well as a Metropolis-coupled MCMC algorithm, both coded in C++ to allow for reasonable execution time using Rcpp (and using probability distribution functions written in C++ from RcppDist).


You can install bggum from CRAN using


Or, you can install the development version from GitHub via



bggum provides tools for Bayesian estimation of GGUM parameters. The package has a vignette with a reasonably in-depth practical guide to Bayesian estimation of GGUM parameters using bggum, so here we simply provide some highlights of available features:

  • Hyper-parameter tuning functions tune_proposals() to tune proposal densities (useful for either sampler) and tune_temperatures() to tune temperature schedules for the Metropolis-coupled MCMC (MC3) sampler
  • An MCMC (ggumMCMC()) and MC3 (ggumMC3()) sampler. Note: For the reasons discussed in Duck-Mayr and Montgomery (2019), we prefer the MC3 sampling approach.
  • Like other IRT models, the GGUM exhibits rotational invariance, so we provide a function (post_process()) to post-process posterior samples to restrict attention to the reflective mode of interest.
  • The results of ggumMCMC() and ggumMC3() are "ggum" class objects, which have a summary() method giving parameter estimates and posterior quantile and standard deviation statistics.
  • We provide item response function (irf()) and item characteristic curve (icc()) plotting functions.

The vignette with a full example and step by step instructions can be accessed via



Before contributing, please consult the contributing guidelines in


GPL (>= 2)

You can’t perform that action at this time.