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Qval (version 1.2.3)

The Q-Matrix Validation Methods Framework

Description

Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) discrimination index (the GDI method) by de la Torre and Chiu (2016) , the Hull method by Najera et al. (2021) , the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) , the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) , the beta method based on signal detection theory by Li and Chen (2024) and Q-matrix validation based on relative fit index by Chen et al. (2013) . Different research methods and iterative procedures during Q-matrix validating are available .

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Version

Install

install.packages('Qval')

Monthly Downloads

364

Version

1.2.3

License

GPL-3

Maintainer

Haijiang Qin

Last Published

June 2nd, 2025

Functions in Qval (1.2.3)

sim.Q

Generate a Random Q-matrix
summary

Summary Methods for Various Objects
print

Print Methods for Various Objects
zOSR

Calculate Over-Specification Rate (OSR)
parallel_iter

A tool for the \(\beta\) Method
update

Update Method for Various Objects
validation

Perform Q-matrix Validation Methods
plot

Plot Methods for Various Qval Objects
sim.MQ

Simulate Mis-specifications in Q-matrix
zQRR

Calculate Q-matrix Recovery Rate (QRR)
sim.data

Generate Response
zTNR

Calculate True-Negative Rate (TNR)
zVRR

Calculate Vector Recovery Ratio (VRR)
zUSR

Calculate Under-Specification Rate (USR)
zTPR

Calculate True-Positive Rate (TPR)
get.Mmatrix

Calculate \(\mathbf{M}\) matrix
Wald.test

Wald Test for Two Q-vectors
extract

Extract Components from Qval Package Objects
get.beta

Calculate \(\beta\)
get.PVAF

Calculate PVAF
get.priority

Priority of Attribute
fit

Calculate Fit Indices
get.Rmatrix

Restriction Matrix
get.R2

Calculate McFadden Pseudo-R2
CDM

Parameter Estimation for Cognitive Diagnosis Models (CDMs) by MMLE/EM or MMLE/BM Algorithm.