Package: hmcdm 2.1.1
Sunbeom Kwon
hmcdm: Hidden Markov Cognitive Diagnosis Models for Learning
Fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times.
Authors:
hmcdm_2.1.1.tar.gz
hmcdm_2.1.1.tar.gz(r-4.5-noble)hmcdm_2.1.1.tar.gz(r-4.4-noble)
hmcdm_2.1.1.tgz(r-4.4-emscripten)hmcdm_2.1.1.tgz(r-4.3-emscripten)
hmcdm.pdf |hmcdm.html✨
hmcdm/json (API)
NEWS
| # Install 'hmcdm' in R: |
| install.packages('hmcdm', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/tmsalab/hmcdm/issues0 issues
- Design_array - Design array
- L_real_array - Observed response times array
- Q_matrix - Q-matrix
- Test_order - Test block ordering of each test version
- Test_versions - Subjects' test version
- Y_real_array - Observed response accuracy array
Last updated 2 years agofrom:cc83242cd3. Checks:3 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 30 2025 |
| R-4.5-linux-x86_64 | OK | Mar 30 2025 |
| R-4.4-linux-x86_64 | OK | Mar 30 2025 |
Exports:ETAmathmcdminv_bijectionvectorOddsRatioQ_list_grandom_QrOmegasim_alphassim_hmcdmsim_RTTPmat
Dependencies:abindbackportsbayesplotcheckmateclicolorspacecrayondescdistributionaldplyrfansifarvergenericsggplot2ggridgesgluegtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellnlmenumDerivpillarpkgconfigplyrposteriorprettyunitsprogressR6RColorBrewerRcppRcppArmadilloRcppParallelreshape2rlangrstantoolsscalesstringistringrtensorAtibbletidyselectutf8vctrsviridisLitewithr
DINA_FOHM
Rendered fromDINA_FOHM.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
DINA_HO_RT_joint
Rendered fromDINA_HO_RT_joint.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
DINA_HO_RT_sep
Rendered fromDINA_HO_RT_sep.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
HMDCM
Rendered fromHMDCM.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
NIDA_indept
Rendered fromNIDA_indept.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
rRUM_indept
Rendered fromrRUM_indept.Rmdusingknitr::rmarkdownon Mar 30 2025.Last update: 2023-01-25
Started: 2022-08-29
Citation
To cite package ‘hmcdm’ in publications use:
Zhang S, Wang S, Chen Y, Kwon S (2023). hmcdm: Hidden Markov Cognitive Diagnosis Models for Learning. R package version 2.1.1, https://CRAN.R-project.org/package=hmcdm.
Corresponding BibTeX entry:
@Manual{,
title = {hmcdm: Hidden Markov Cognitive Diagnosis Models for
Learning},
author = {Susu Zhang and Shiyu Wang and Yinghan Chen and Sunbeom
Kwon},
year = {2023},
note = {R package version 2.1.1},
url = {https://CRAN.R-project.org/package=hmcdm},
}
Readme and manuals
hmcdm
The goal of hmcdm is to provide an implementation of Hidden Markov
Cognitive Diagnosis Models for Learning.
Installation
You can install hmcdm from CRAN using:
install.packages("hmcdm")
Or, you can be on the cutting-edge development version on GitHub using:
if(!requireNamespace("remotes")) install.packages("remotes")
remotes::install_github("tmsalab/hmcdm")
Usage
To use hmcdm, load the package using:
library("hmcdm")
Authors
Susu Zhang, Shiyu Wang, Yinghan Chen, and Sunbeom Kwon
Citing the hmcdm package
To ensure future development of the package, please cite hmcdm package
if used during an analysis or simulation study. Citation information for
the package may be acquired by using in R:
citation("hmcdm")
License
GPL (>= 2)
