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ohoegdm

The goal of ohoegdm is to provide an implementation of the Ordinal Higher-order Exploratory General Diagnostic Model for Polytomous Data as described by Culpepper and Balamuta (2023).

Installation

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

install.packages("ohoegdm")

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

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

Usage

To use ohoegdm, load the package using:

library("ohoegdm")

From here, the OHO-EGDM model can be estimated using:

my_model = ohoegdm::ohoegdm(
  y = <data>,
  k = <k>,
  m = <item-responses-categories>,
  order = <model-interaction-order>
)

Authors

Steven Andrew Culpepper and James Joseph Balamuta

Citing the ohoegdm package

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

citation("ohoegdm")

License

GPL (>= 2)

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Version

Install

install.packages('ohoegdm')

Monthly Downloads

283

Version

0.1.1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Maintainer

James Balamuta

Last Published

September 27th, 2025

Functions in ohoegdm (0.1.1)

gen_bijectionvector

Generate a vector to map polytomous vector to integers
sim_slcm

Simulate Ordinal Item Data from a Sparse Latent Class Model
ohoegdm

Ordinal Higher-Order General Diagnostic Model under the Exploratory Framework (OHOEGDM)
ohoegdm-package

ohoegdm: Ordinal Higher-Order Exploratory General Diagnostic Model for Polytomous Data
GenerateAtable

Generate tables that store different design elements