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# GDINA Package for Cognitively Diagnostic Analyses

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## How to cite the package

Ma, W. & de la Torre, J. (2020). GDINA: An R Package for Cognitive
Diagnosis Modeling. Journal of Statistical Software, 93(14), 1-26.
<https://doi.org/10.18637/jss.v093.i14>

Visit the package website <https://wenchao-ma.github.io/GDINA/> for
examples, tutorials and more information.

## Learning resources

-   Watch [Ma and de la Torre’s (2019) NCME digital module
    5](https://ncme.elevate.commpartners.com/) on a gentle introduction
    to the G-DINA model framework and the use of graphical user
    interface for CDM analyses

-   Check [the book chapter by Ma
    (2019)](https://doi.org/10.1007/978-3-030-05584-4_29) on an
    introduction to the GDINA package

-   Check [de la Torre and Akbay’s (2019) article](https://ejer.com.tr/)
    on how to conduct various CDM analyses using the graphical user
    interface

-   Check [Shi, et al (2021)](https://doi.org/10.3390/psych3040052) on
    how to use this package, along with other R packages for CDM
    analyses

## Features of the package

-   Estimating G-DINA model and a variety of widely-used models subsumed
    by the G-DINA model, including the DINA model, DINO model,
    additive-CDM (A-CDM), linear logistic model (LLM), reduced
    reparametrized unified model (RRUM), multiple-strategy DINA model
    for dichotomous responses
-   Estimating models within the G-DINA model framework using
    user-specified design matrix and link functions
-   Estimating Bugs-DINA, DINO and G-DINA models for dichotomous
    responses
-   Estimating sequential G-DINA model for ordinal and nominal responses
-   Estimating the generalized multiple-strategy cognitive diagnosis
    models (experimental)
-   Estimating the diagnostic tree model (experimental)
-   Estimating multiple-choice models
-   Modelling independent, saturated, higher-order, loglinear smoothed,
    and structured joint attribute distribution
-   Accommodating multiple-group model analysis
-   Imposing monotonic constrained success probabilities
-   Accommodating binary and polytomous attributes
-   Validating Q-matrix under the general model framework
-   Evaluating absolute and relative item and model fit
-   Comparing models at the test and item levels
-   Detecting differential item functioning using Wald and likelihood
    ratio test
-   Simulating data based on all aforementioned CDMs
-   Providing graphical user interface for users less familiar with R

## Installation

The stable version of GDINA should be installed from R CRAN at
[here](https://CRAN.R-project.org/package=GDINA)

To install this package from source:

1)  Windows users may need to install the
    [Rtools](https://CRAN.R-project.org/bin/windows/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](https://apps.apple.com/ca/app/xcode/id497799835?mt=12) 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).

2)  Install the `devtools` package (if necessary), and install the
    package from the Github source code.

``` r
# install.packages("devtools")
devtools::install_github("Wenchao-Ma/GDINA")
```
