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)
Ma, W. & de la Torre, J. (2019). GDINA: The generalized DINA model framework. R package version 2.5. Retrived from https://CRAN.R-project.org/package=GDINA
Visit the package website https://wenchao-ma.github.io/GDINA for examples and tutorials.
NCME digital module 5 on the G-DINA model and the use of graphical user interface for CDM analyses
Check de la Torre and Akbay’s (2019) article on how to conduct various CDM analyses using the graphical user interface
To install this package from source:
Windows users may need to install the Rtools and include the checkbox option of installing Rtools to their path for easier command line usage. Mac users will have to download the necessary tools from the Xcode and its related command line tools (found within Xcode’s Preference Pane under Downloads/Components); most Linux distributions should already have up to date compilers (or if not they can be updated easily).
devtools package (if necessary), and install the
package from the Github source code.
The stable version of GDINA should be installed from R CRAN at here
ICLA()function for attribute profile estimation
print.GDINA()` prints valid number of individuals by default now
Qvalnot work when estimated number of individuals in some latent classes are 0
extractonly gives valid data
att.strargument of the
GDINAfunction has been updated
modelcompfunction to provide selected models directly
logLikfunction to be consistent with default S3 methods
GDINAfunction has been largely rewritten for both flexibility and speed. Users are now allowed to fit
Bugsmodels, and define models by providing design matrix and link functions. For joint attribute distribution, in addition to saturated and higher-order models, users are now allowed to fit independent model and loglinear model. Code for joint attribute distribution modelling has been restructured as well. Other major updates include model fit evaluation using M2 statistics and other limited information measures, item-level model selection using likelihood ratio test and score test, classification accuracy evaluation indices, bootstrap standard error estimation, etc.
modelcompin the version 1.4.1
startGDINAto report absolute fit statistics
GDINAfunction arguments were modified
deviancefunctions are modified
anovafunction is changed and multiple models can be comparied
summaryfunction is changed
extractfunction are not rounded
diffunction when DIF items are specified
LC2LGto find equivalent latent groups
scoreto find score functions
summary.GDINAfunction for multiple group estimation
itemfitfunction for missing data