  In this article, the JAGS software program is systematically introduced to
fit common Bayesian cognitive diagnosis models (CDMs), including the
deterministic inputs, noisy "and" gate (DINA) model, the deterministic inputs,
noisy "or" gate (DINO) model, the linear logistic model, the reduced
reparameterized unified model (rRUM), and the log-linear CDM (LCDM). The
unstructured latent structural model and the higher-order latent structural
model are both introduced. We also show how to extend those models to consider
the polytomous attributes, the testlet effect, and the longitudinal diagnosis.
Finally, an empirical example is presented as a tutorial to illustrate how to
use the JAGS codes in R.
