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The goal of rrum is to provide an implementation of Gibbs sampling algorithm for Bayesian Estimation of Reduced Reparameterized Unified Model (rrum), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.


You can install rrum from CRAN using:


Or, you can be on the cutting-edge development version on GitHub using:

# install.packages('remotes')


To use rrum, load the package using:


From here, the rRUM model can be estimated using:

rrum_model = rrum(<data>, <q>)

Additional parameters can be accessed with:

rrum_model = rrum(<data>, <q>, chain_length = 10000L,
                  as = 1, bs = 1, ag = 1, bg = 1,
                  delta0 = rep(1, 2^ncol(Q)))

rRUM item data can be simulated using:

# Set a seed for reproducibility

# Setup Parameters
N = 15   # Number of Examinees / Subjects
J = 10   # Number of Items
K = 2    # Number of Skills / Attributes

# Simulate identifiable Q matrix
Q = sim_q_matrix(J, K)

# Penalties for failing to have each of the required attributes
rstar  = .5 * Q

# The probabilities of answering each item correctly for individuals 
# who do not lack any required attribute
pistar = rep(.9, J)

# Latent Class Probabilities
pis = c(.1, .2, .3, .4)

# Generate latent attribute profile with custom probability (N subjects by K skills)
subject_alphas = sim_subject_attributes(N, K, prob = pis)

# Simulate rrum items
rrum_items = simcdm::sim_rrum_items(Q, rstar, pistar, subject_alphas)


Steven Andrew Culpepper, Aaron Hudson, and James Joseph Balamuta

Citing the rrum package

To ensure future development of the package, please cite rrum package if used during an analysis or simulation study. Citation information for the package may be acquired by using in R:



GPL (>= 2)