Package: dina 2.0.2

James Joseph Balamuta

dina: Bayesian Estimation of DINA Model

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

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

dina_2.0.2.tar.gz
dina_2.0.2.zip(r-4.7-x86_64)dina_2.0.2.zip(r-4.7-arm64)dina_2.0.2.zip(r-4.6-x86_64)dina_2.0.2.zip(r-4.6-arm64)dina_2.0.2.zip(r-4.5-x86_64)
dina_2.0.2.tgz(r-4.6-x86_64)dina_2.0.2.tgz(r-4.6-arm64)dina_2.0.2.tgz(r-4.5-x86_64)dina_2.0.2.tgz(r-4.5-arm64)
dina_2.0.2.tar.gz(r-4.7-arm64)dina_2.0.2.tar.gz(r-4.7-x86_64)dina_2.0.2.tar.gz(r-4.6-arm64)dina_2.0.2.tar.gz(r-4.6-x86_64)
dina_2.0.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dina/json (API)

# Install 'dina' in R:

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

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

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

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3

On CRAN:

armadillo bayesian gibbs-sampler irt item-response-theory psychometrics rcpp rcpparmadillo openblas cpp

3.90 score 16 stars 5 scripts 463 downloads 2 exports 4 dependencies

Last updated from:b9011d75ed. Checks:15 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	95
linux-devel-x86_64	OK	127
source / vignettes	OK	225
linux-release-arm64	OK	93
linux-release-x86_64	OK	116
macos-release-arm64	OK	108
macos-release-x86_64	OK	300
macos-oldrel-arm64	OK	120
macos-oldrel-x86_64	OK	270
windows-devel-arm64	OK	237
windows-devel-x86_64	OK	142
windows-release-arm64	OK	199
windows-release-x86_64	OK	96
windows-oldrel-x86_64	OK	96
wasm-release	OK	91