GDINA2.9.4 package

The Generalized DINA Model Framework

att.structure

Generate hierarchical attribute structures

attributepattern

Generate all possible attribute patterns

autoGDINA

Q-matrix validation, model selection and calibration in one run

bdiagMatrix

Create a block diagonal matrix

bootSE

Calculating standard errors and variance-covariance matrix using boots...

CA

Calculate classification accuracy

cjoint

Combine R Objects by Columns

ClassRate

Classification Rate Evaluation

designmatrix

Generate design matrix

dif

Differential item functioning for cognitive diagnosis models

DTM

Experimental function for diagnostic multiple-strategy CDMs

extract

extract elements from objects of various classes

GDINA-package

The Generalized DINA Model Framework

GDINA

CDM calibration under the G-DINA model framework

GMSCDM

Estimating multiple-strategy cognitive diagnosis models

ILCA

Iterative latent-class analysis

indlogLik

Extract log-likelihood for each individual

indlogPost

Extract log posterior for each individual

itemfit

Item fit statistics

itemparm

extract item parameters (deprecated)

LC2LG

Transformation between latent classes and latent groups

MCmodel

Multiple-choice models

modelcomp

Item-level model comparison using Wald, LR or LM tests

modelfit

Model fit statistics

monocheck

This function checks if monotonicity is violated

npar

Calculate the number of parameters

personparm

calculate person (incidental) parameters

plot.GDINA

Create plots for GDINA estimates

plot.itemfit

Item fit plots

plot.Qval

Mesa plot for Q-matrix validation

Qval

Q-matrix validation

rowMatch

Count the frequency of a row vector in a data frame

score

Score function

simDTM

Simulating data for diagnostic tree model

simGDINA

Data simulation based on the G-DINA models

startGDINA

Graphical user interface of the GDINA function

unique_only

Unique values in a vector

unrestrQ

Generate unrestricted Qc matrix from an restricted Qc matrix

A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019).

  • Maintainer: Wenchao Ma
  • License: GPL-3
  • Last published: 2023-07-01