The Generalized DINA Model Framework
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 boots...
Calculate classification accuracy
Combine R Objects by Columns
Classification Rate Evaluation
Generate design matrix
Differential item functioning for cognitive diagnosis models
Experimental function for diagnostic multiple-strategy CDMs
extract elements from objects of various classes
The Generalized DINA Model Framework
CDM calibration under the G-DINA model framework
Estimating multiple-strategy cognitive diagnosis models
Iterative latent-class analysis
Extract log-likelihood for each individual
Extract log posterior for each individual
Item fit statistics
extract item parameters (deprecated)
Transformation between latent classes and latent groups
Multiple-choice models
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
Item fit plots
Mesa plot for Q-matrix validation
Q-matrix validation
Count the frequency of a row vector in a data frame
Score function
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
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).
Useful links