Function reference • hmcdm

All functions
`Design_array`	Design array
`ETAmat()`	Generate ideal response matrix
`L_real_array`	Observed response times array
`OddsRatio()`	Compute item pairwise odds ratio
`Q_list_g()`	Generate a list of Q-matrices for each examinee.
`Q_matrix`	Q-matrix
`TPmat()`	Generate monotonicity matrix
`Test_versions`	Subjects' test version
`Y_real_array`	Observed response accuracy array
`hmcdm-package` `_PACKAGE`	hmcdm: Hidden Markov Cognitive Diagnosis Models for Learning
`hmcdm()`	Gibbs sampler for learning models
`inv_bijectionvector()`	Convert integer to attribute pattern
`pp_check(<hmcdm>)`	Graphical posterior predictive checks for hidden Markov cognitive diagnosis model
`rOmega()`	Generate a random transition matrix for the first order hidden Markov model
`random_Q()`	Generate random Q matrix
`sim_RT()`	Simulate item response times based on Wang et al.'s (2018) joint model of response times and accuracy in learning
`sim_alphas()`	Generate attribute trajectories under the specified hidden Markov models
`sim_hmcdm()`	Simulate responses from the specified model (entire cube)
`print(<summary.hmcdm>)` `summary(<hmcdm>)`	Summarizing Hidden Markov Cognitive Diagnosis Model Fits
`Test_order`	Test block ordering of each test version

Reference