output: github_document

```{r, echo = FALSE}

[](https://www.repostatus.org/)

[](https://github.com/Wenchao-Ma/GDINA/actions/workflows/R-CMD-check.yaml)

[](https://cran.r-project.org/package=GDINA)

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](https://doi.org/10.18637/jss.v093.i14)

Visit the package website [https://wenchao-ma.github.io/GDINA/](https://wenchao-ma.github.io/GDINA/) for examples, tutorials and more information.

* 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

* 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

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 gh-installation, eval = FALSE}