This package is considered a duplicate. The official version of this package is found at:https://tmsalab.r-universe.dev/hmcdm

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

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 = c('https://cran.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

openblas cpp openmp

3.56 score 12 scripts 187 downloads 11 exports 54 dependencies

Last updated 2 years agofrom:cc83242cd3. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Dec 30 2024
R-4.5-linux-x86_64	OK	Dec 30 2024

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 colorspace crayon desc distributional dplyr fansi farver generics ggplot2 ggridges glue gtable hms isoband labeling lattice lifecycle magrittr MASS Matrix matrixStats mgcv munsell nlme numDeriv pillar pkgconfig plyr posterior prettyunits progress R6 RColorBrewer Rcpp RcppArmadillo RcppParallel reshape2 rlang rstantools scales stringi stringr tensorA tibble tidyselect utf8 vctrs viridisLite withr