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:

`install.packages("rrum")`

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

```
if(!requireNamespace("devtools")) install.packages("devtools")
::install_github("tmsalab/rrum") devtools
```

To use `rrum`

, load the package using:

```
library("rrum")
#> Loading required package: simcdm
```

From here, the rRUM model can be estimated using:

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

Additional parameters can be accessed with:

```
= rrum(<data>, <q>, chain_length = 10000L,
rrum_model 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
= 15 # Number of Examinees / Subjects
N = 10 # Number of Items
J = 2 # Number of Skills / Attributes
K
# Simulate identifiable Q matrix
= sim_q_matrix(J, K)
Q
# Penalties for failing to have each of the required attributes
= .5 * Q
rstar
# The probabilities of answering each item correctly for individuals
# who do not lack any required attribute
= rep(.9, J)
pistar
# Latent Class Probabilities
= c(.1, .2, .3, .4)
pis
# Generate latent attribute profile with custom probability (N subjects by K skills)
= sim_subject_attributes(N, K, prob = pis)
subject_alphas
# Simulate rrum items
= simcdm::sim_rrum_items(Q, rstar, pistar, subject_alphas) rrum_items
```

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*:

`citation("rrum")`

GPL (>= 2)