Bayesian Psychometric Measurement Using 'Stan'
Maximum likelihood based information criteria
Bayes factor for model comparisons
Item, attribute, and test-level discrimination indices
Fit Bayesian diagnostic classification models
Posterior predictive model checks for assessing model fit
Log marginal likelihood calculation
Extract the log-likelihood of an estimated model
Relative fit for Bayesian models
Estimate the fit statistic for diagnostic classification models
Fit Bayesian diagnostic classification models
Determine if code is executed interactively or in pkgdown
Extract components of a measrfit object
measr: Bayesian Psychometric Measurement Using 'Stan'
S7 class for measrdcm objects
Add model evaluation metrics model objects
Q-matrix validation
Objects exported from other packages
Estimate the reliability of a diagnostic classification model
Posterior draws of respondent proficiency
S7 classes for estimation specifications
Tidy eval helpers
Yen's statistic for local item dependence
Estimate diagnostic classification models (also called cognitive diagnostic models) with 'Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated 'Stan' code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.
Useful links