GDINA

Calculate classification accuracy

Classification Rate Evaluation

Experimental function for diagnostic multiple-strategy CDMs

CDM calibration under the G-DINA model framework

The Generalized DINA Model Framework

Estimating multiple-strategy cognitive diagnosis models

Iterative latent-class analysis

Transformation between latent classes and latent groups

Multiple-choice models

Q-matrix validation

Generate hierarchical attribute structures

Generate all possible attribute patterns

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

Create a block diagonal matrix

Calculating standard errors and variance-covariance matrix using bootstrap methods

Combine R Objects by Columns

Generate design matrix

Differential item functioning for cognitive diagnosis models

Examination for the Certificate of Proficiency in English (ECPE) data

extract elements from objects of various classes

Tatsuoka's fraction subtraction data

Extract log-likelihood for each individual

Extract log posterior for each individual

Item fit statistics

extract item parameters (deprecated)

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

Model fit statistics

This function checks if monotonicity is violated

Calculate the number of parameters

calculate person (incidental) parameters

Create plots for GDINA estimates

Mesa plot for Q-matrix validation

Item fit plots

Count the frequency of a row vector in a data frame

Score function

Simulated data (10 items, G-DINA model)

Simulated data (10 items, MC-DINA model)

Simulated data (20 items, sequential G-DINA model)

Simulated data (21 items, sequential DINA model)

Simulated data (30 items, DINA model)

Simulated data (30 items, G-DINA model)

Simulated data (30 items, polytomous G-DINA model)

Simulating data for diagnostic tree model

Data simulation based on the G-DINA models

Graphical user interface of the GDINA function

Unique values in a vector

Generate unrestricted Qc matrix from an restricted Qc matrix