Package: ACTCD 1.4-0

ACTCD: Asymptotic Classification Theory for Cognitive Diagnosis

Cluster analysis for cognitive diagnosis based on the Asymptotic Classification Theory (Chiu, Douglas & Li, 2009; <doi:10.1007/s11336-009-9125-0>). Given the sample statistic of sum-scores, cluster analysis techniques can be used to classify examinees into latent classes based on their attribute patterns. In addition to the algorithms used to classify data, three labeling approaches are proposed to label clusters so that examinees' attribute profiles can be obtained.

Authors:Chia-Yi Chiu [aut], Wenchao Ma [aut, cre]

ACTCD_1.4-0.tar.gz
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ACTCD_1.4-0.tgz(r-4.6-x86_64)ACTCD_1.4-0.tgz(r-4.6-arm64)ACTCD_1.4-0.tgz(r-4.5-x86_64)ACTCD_1.4-0.tgz(r-4.5-arm64)
ACTCD_1.4-0.tar.gz(r-4.7-arm64)ACTCD_1.4-0.tar.gz(r-4.7-x86_64)ACTCD_1.4-0.tar.gz(r-4.6-arm64)ACTCD_1.4-0.tar.gz(r-4.6-x86_64)
ACTCD_1.4-0.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
ACTCD/json (API)

# Install 'ACTCD' in R:
install.packages('ACTCD', repos = c('https://wenchao-ma.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • perm3 - The partial orders of the attribute patterns for 'labeling'
  • perm4 - The partial orders of the attribute patterns for 'labeling'
  • sim.dat - Simulated data
  • sim.Q - A complete Q-matrix used to generate 'sim.dat'.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 7 scripts 254 downloads 5 exports 59 dependencies

Last updated from:17a5a8a157. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK157
linux-devel-x86_64OK148
source / vignettesOK278
linux-release-arm64OK160
linux-release-x86_64OK139
macos-release-arm64OK104
macos-release-x86_64OK211
macos-oldrel-arm64OK135
macos-oldrel-x86_64OK211
windows-develOK151
windows-releaseOK132
windows-oldrelOK131
wasm-releaseOK117

Exports:alphacd.clusteretalabelingnpar.CDM

Dependencies:alabamabase64encbslibcachemclicodetoolscommonmarkcpp11digestfarverfastmapfontawesomeforeachfsfuturefuture.applyGDINAggplot2globalsgluegtablehtmltoolshttpuvisobanditeratorsjquerylibjsonlitelabelinglaterlifecyclelistenvmagrittrMASSmemoisemimenloptrnumDerivotelparallellypromisesR.methodsS3R6rappdirsRColorBrewerRcppRcppArmadillorlangRsolnpS7sassscalesshinyshinydashboardsourcetoolstruncnormvctrsviridisLitewithrxtable