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:

To use `rrum`

, load the package using:

From here, the rRUM model can be estimated using:

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
set.seed(888)
# 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

`rrum`

packageTo 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)