Package: hmcdm 2.1.3

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:Susu Zhang [aut], Shiyu Wang [aut], Yinghan Chen [aut], Sunbeom Kwon [aut, cre]

hmcdm_2.1.3.tar.gz
hmcdm_2.1.3.zip(r-4.7)hmcdm_2.1.3.zip(r-4.6)hmcdm_2.1.3.zip(r-4.5)
hmcdm_2.1.3.tgz(r-4.6-x86_64)hmcdm_2.1.3.tgz(r-4.6-arm64)hmcdm_2.1.3.tgz(r-4.5-x86_64)hmcdm_2.1.3.tgz(r-4.5-arm64)
hmcdm_2.1.3.tar.gz(r-4.7-arm64)hmcdm_2.1.3.tar.gz(r-4.7-x86_64)hmcdm_2.1.3.tar.gz(r-4.6-arm64)hmcdm_2.1.3.tar.gz(r-4.6-x86_64)
hmcdm_2.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
hmcdm/json (API)

# Install 'hmcdm' in R:

install.packages('hmcdm', repos = c('https://tmsalab.r-universe.dev', 'https://cloud.r-project.org'))

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

Datasets:

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

On CRAN:

cognitive-diagnostic-models psychometrics rcpp rcpparmadillo openblas cpp openmp

6.26 score 8 stars 19 scripts 501 downloads 11 exports 50 dependencies

Last updated from:0edc943f20. Checks:13 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	247
linux-devel-x86_64	OK	191
source / vignettes	OK	489
linux-release-arm64	OK	211
linux-release-x86_64	OK	162
macos-release-arm64	OK	103
macos-release-x86_64	OK	314
macos-oldrel-arm64	OK	170
macos-oldrel-x86_64	OK	278
windows-devel	OK	165
windows-release	OK	210
windows-oldrel	OK	186
wasm-release	OK	155

Exports:ETAmat hmcdm inv_bijectionvector OddsRatio Q_list_g random_Q rOmega sim_alphas sim_hmcdm sim_RT TPmat

Dependencies:abind backports bayesplot checkmate cli cpp11 crayon desc distributional dplyr farver generics ggplot2 ggridges glue gtable hms isoband labeling lifecycle magrittr matrixStats numDeriv pillar pkgconfig plyr posterior prettyunits progress purrr R6 RColorBrewer Rcpp RcppArmadillo RcppParallel reshape2 rlang rstantools S7 scales stringi stringr tensorA tibble tidyr tidyselect utf8 vctrs viridisLite withr

DINA_FOHM

Load the spatial rotation data | (1) Simulate responses and response times based on the DINA_FOHM model | (2) Run the MCMC to sample parameters from the posterior distribution | (3) Check for parameter estimation accuracy | (4) Evaluate the fit of the model to the observed response

Rendered fromDINA_FOHM.Rmdusingknitr::rmarkdown