# Hidden Markov Cognitive Diagnosis Models for Learning

## Documentation for package ‘hmcdm’ version 2.1.3

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

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