Package: simcdm 0.1.2

James Joseph Balamuta

simcdm: Simulate Cognitive Diagnostic Model ('CDM') Data

Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi:10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

Authors:James Joseph Balamuta [aut, cre, cph], Steven Andrew Culpepper [aut, cph], Aaron Hudson [ctb, cph]

simcdm_0.1.2.tar.gz
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simcdm.pdf |simcdm.html✨
simcdm/json (API)
NEWS

# Install 'simcdm' in R:
install.packages('simcdm', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/tmsalab/simcdm/issues

Pkgdown site:https://tmsalab.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.95 score 2 packages 15 scripts 254 downloads 10 exports 2 dependencies

Last updated 1 years agofrom:026450022d. Checks:2 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKFeb 22 2025
R-4.5-linux-x86_64OKFeb 22 2025

Exports:attribute_bijectionattribute_classesattribute_inv_bijectionsim_dina_attributessim_dina_classsim_dina_itemssim_eta_matrixsim_q_matrixsim_rrum_itemssim_subject_attributes

Dependencies:RcppRcppArmadillo

Overview of simcdm

James Joseph Balamuta

Rendered fromoverview-simcdm.Rmdusingknitr::rmarkdownon Feb 22 2025.

Last update: 2019-01-31
Started: 2019-01-30

Using simcdm in R packages

James Joseph Balamuta

Rendered fromsimcdm-in-packages.Rmdusingknitr::rmarkdownon Feb 22 2025.

Last update: 2019-01-30
Started: 2019-01-30

Citation

To cite the 'simcdm' package in publications use:

Balamuta, J. J. (2021). Bayesian estimation of restricted latent class models: Extending priors, link functions, and structural models. University of Illinois Urbana-Champaign.

Balamuta, J. J., Culpepper, S. A., and Hudson A. (2019) simcdm: Simulate Cognitive Diagnostic Model (CDM) Data. URL https://cran.r-project.org/package=simcdm.

Corresponding BibTeX entries: 

  @PhdThesis{,
    title = {Bayesian estimation of restricted latent class models:
      Extending priors, link functions, and structural models},
    author = {James Joseph Balamuta},
    school = {University of Illinois Urbana-Champaign},
    doi = {10.1080/00273171.2021.1985949},
    year = {2021},
  }

  @Manual{,
    title = {{simcdm: Simulate Cognitive Diagnostic Model (CDM) Data}},
    author = {James Joseph Balamuta and Steven Andrew Culpepper and
      Aaron Hudson},
    year = {2019},
  }

Readme and manuals

simcdm

The goal of simcdm is to provide flexible ways to simulate data under cognitive diagnostic models.

Installation

You can install simcdm from GitHub with:

# install.packages("remotes")
remotes::install_github("tmsalab/simcdm")

Usage

To use simcdm, load the package using:

library("simcdm")

Overview

There are four distinct sets of functions within the package:

  • Attributes: attribute_classes(), attribute_bijection(), attribute_inv_bijection(), and sim_subject_attributes().
  • Matrix: sim_q_matrix() and sim_eta_matrix()
  • Deterministic Input, Noisy And Gate (DINA): sim_dina_items() and sim_dina_attributes()
  • reduced Reparameterized Unified Model (rRUM): sim_rrum_items()

Functions that use random numbers to simulate values are named with the prefix of sim_*(). This is done to allow for functions to be quickly identified and used through autocomplete inside of the RStudio IDE or VS Code. At a later time, the attribute_*() will likely be moved to a different package.

For more details, please see the package vignettes:

Authors

James Joseph Balamuta and Steven Andrew Culpepper with contributions from Aaron Hudson.

Citing the simcdm package

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

citation("simcdm")

License

GPL (>= 2)