mc_sim_Q

Artificial Q-matrix for a 30-item test measuring 5 attributes.

datasets

Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>, Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.

Keiichiro Hijikata

variationalDCM

Variational Bayesian Estimation for Diagnostic Classification
Models

Motonori Oka

Kazuhiro Yamaguchi

Kensuke Okada

mc_sim_Q function

A matrix with components<dl>
<dt>column 1</dt>
<dd>Item number</dd><dt>column 2</dt>
<dd>Stem</dd><dt>column 3 to end</dt>
<dd>attributes</dd>
</dl>

Format

Artificial Q-matrix for MC-DINA model — mc_sim_Q

A matrix with components<dl>
<dt>column 1</dt>
<dd>Item number</dd>

<dt>column 2</dt>
<dd>Stem</dd>

<dt>column 3 to end</dt>
<dd>attributes</dd>


</dl>

mc_sim_Q: Artificial Q-matrix for MC-DINA model

Description

Usage

Arguments

Format

References