Stochastic EM Algorithm for High-Dimensional Cognitive Diagnosis Models

Provides stochastic expectation-maximization (stEM) algorithms for estimating high-dimensional cognitive diagnosis models. The package implements stochastic EM algorithms for cognitive diagnosis models with a large number of attributes. It includes estimation functions and example datasets for model fitting and analysis. The methods are described in Ma, W., Wang, K., and Xu, G. (Accepted). "Parameter estimation of cognitive diagnosis models with stochastic EM algorithm." Behaviometrika.

HighDimenCDM: High-Dimensional Cognitive Diagnosis Models

To cite this paper:

• Ma, W., Wang, K., & Xu, G. (Accepted). Parameter estimation of cognitive diagnosis models with stochastic EM algorithm. Behaviormetrika.

• Mei, Y., Ma, W., Wang, K., & Xu, G. (2026) HighDimenCDM: High-Dimensional Cognitive Diagnosis Models. R package.

Features

This package aims to provide tools for high-dimensional cognitive diagnosis models. Currently, it supports the following features:

• Implements stochastic expectation-maximization algorithms for high-dimensional cognitive diagnosis models

• Efficient Gibbs sampling for attribute estimation

• Supports binary response cognitive diagnosis models with binary attributes

Installation

Install the development version of HighDimenCDM from GitHub using the devtools package.

# install.packages("devtools")
#devtools::install_github("HighDimenCDM")