Sparse Latent Class Model for Cognitive Diagnosis

Perform a Bayesian estimation of the exploratory Sparse Latent Class Model for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) .

slcm

The goal of slcm is to provide an implementation of the exploratory Sparse Latent Class Model (SLCM) for Binary Data described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) doi:10.1007/s11336-019-09693-2.

This package contains a new implementation of the proposed SLCM based on the paper. You may find original papers implementation in the inst/ folder of the package.

Installation

You can install the released version of slcm from CRAN with:

install.packages("slcm")

Or, you can be on the cutting-edge development version on GitHub using:

# install.packages("devtools")
devtools::install_github("tmsalab/slcm")

Usage

To use slcm, load the package using:

library("slcm")

From here, the SLCM model can be estimated using:

model_slcm = slcm::slcm(
  y = <data>,
  k = <k>
)

Authors

James Joseph Balamuta and Steven Andrew Culpepper

Citing the slcm package

To ensure future development of the package, please cite slcm package if used during an analysis or simulation study. Citation information for the package may be acquired by using in R:

citation("slcm")

License

GPL (>= 2)