Package: NPCDTools 1.2.0

NPCDTools: The Nonparametric Classification Methods for Cognitive Diagnosis

Statistical tools for analyzing cognitive diagnosis (CD) data collected from small settings using the nonparametric classification (NPCD) framework. The core methods of the NPCD framework includes the nonparametric classification (NPC) method developed by Chiu and Douglas (2013) <doi:10.1007/s00357-013-9132-9> and the general NPC (GNPC) method developed by Chiu, Sun, and Bian (2018) <doi:10.1007/s11336-017-9595-4> and Chiu and Köhn (2019) <doi:10.1007/s11336-019-09660-x>. An extension of the NPCD framework included in the package is the nonparametric method for multiple-choice items (MC-NPC) developed by Wang, Chiu, and Koehn (2023) <doi:10.3102/10769986221133088>. Functions associated with various extensions concerning the evaluation, validation, and feasibility of the CD analysis are also provided. These topics include the completeness of Q-matrix, Q-matrix refinement method, as well as Q-matrix estimation.

Authors:Chia-Yi Chiu [aut, cph], Weixuan Xiao [aut, cre], Hans Friedrich Köhn [aut], Yu Wang [aut], Xiran Wen [aut]

NPCDTools_1.2.0.tar.gz
NPCDTools_1.2.0.zip(r-4.7-any)NPCDTools_1.2.0.zip(r-4.6-any)NPCDTools_1.2.0.zip(r-4.5-any)
NPCDTools_1.2.0.tgz(r-4.6-any)NPCDTools_1.2.0.tgz(r-4.5-any)
NPCDTools_1.2.0.tar.gz(r-4.7-any)NPCDTools_1.2.0.tar.gz(r-4.6-any)
NPCDTools_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
NPCDTools/json (API)

# Install 'NPCDTools' in R:
install.packages('NPCDTools', repos = c('https://weixuan-xiao.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

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

1.30 score 1 scripts 335 downloads 19 exports 65 dependencies

Last updated from:4b2d00c635. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK137
source / vignettesOK190
linux-releaseOK128
macos-releaseOK168
macos-oldrelOK187
windows-develOK70
windows-releaseOK67
windows-oldrelOK64
wasm-releaseOK132

Exports:AARbestQpermcorrection.ratedistractor.checkGNPCmcdat.generateMCNPCmcQ.generateNPCPARQ.completenessQ.generateQ.implausibleQ.improperQRretention.rateRRrun_gnpc_appTSQE

Dependencies:alabamabase64encbslibcachemclicodetoolscommonmarkcpp11digestfarverfastmapfontawesomeforeachfsfuturefuture.applyGDINAggplot2globalsglueGPArotationgtablegtoolshtmltoolshttpuvisobanditeratorsjquerylibjsonlitelabelinglaterlatticelifecyclelistenvmagrittrMASSMatrixmemoisemimemnormtnlmenloptrnumDerivotelparallellypromisespsychR6rappdirsRColorBrewerRcppRcppArmadillorlangRsolnpS7sassscalesshinyshinydashboardsourcetoolstruncnormvctrsviridisLitewithrxtable

Readme and manuals

Help Manual

Help pageTopics
Attribute-wise agreement rateAAR
Column permutation of a Q-matrix with respect to a benchmark Q-matrix.bestQperm
Correction rate of a Q-matrix refinement methodcorrection.rate
Detect the implausible and improper distractors in a Q-Matrix for multiple-choice itemsdistractor.check
Estimation of examinees' attribute profiles using the GNPC methodGNPC
Generation of MC Responsesmcdat.generate
The Nonparametric Classification Method for Multiple-Choice ItemsMCNPC
Generation of Plausible and Proper Q-matrix with Coded DistractorsmcQ.generate
Estimation of examinees' attribute profiles using the NPC methodNPC
Pattern-wise agreement ratePAR
Plot Diagnostics for GNPCplot.GNPC
Print the Summary of Distractor Check Resultsprint.distractor.check
Print the Summary of a GNPC Objectprint.GNPC
Print the Summary of an NPC Objectprint.NPC
Print Summary of a Qcompleteness Objectprint.Qcompleteness
Print Summary of the Q-Matrix Refinement Resultprint.Qrefine
Q-Matrix from Ozaki (2015)Q_Ozaki
Check the completeness status of a binary Q-matrixQ.completeness
Generation of dichotomous Q-matrixQ.generate
Generation of a Q-matrix that contains implausible MC itemsQ.implausible
Generation of a Q-matrix with improper MC items that are not proper based on a 'plausible and proper' QQ.improper
Q-matrix refinement methodQR
Retention rate of a Q-matrix refinement methodretention.rate
Entry-wise and vector-wise agreement rate between two Q-matricesRR
Launch the GNPC Shiny apprun_gnpc_app
Two-step Q-matrix estimation methodTSQE