ML2Pvae: Variational Autoencoder Models for IRT Parameter Estimation
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) <doi:10.1109/IJCNN.2019.8852333>. This package provides easy-to-use functions which create a variational autoencoder (VAE) to be used for parameter estimation in Item Response Theory (IRT) - namely the Multidimensional Logistic 2-Parameter (ML2P) model. To use a neural network as such, nontrivial modifications to the architecture must be made, such as restricting the nonzero weights in the decoder according to some binary matrix Q. The functions in this package allow for straight-forward construction, training, and evaluation so that minimal knowledge of 'tensorflow' or 'keras' is required.
| Version: |
1.0.0.1 |
| Depends: |
R (≥ 3.6) |
| Imports: |
keras (≥ 2.3.0), reticulate (≥ 1.0), tensorflow (≥ 2.2.0), tfprobability (≥ 0.11.0) |
| Suggests: |
knitr, rmarkdown, testthat, R.rsp |
| Published: |
2022-05-23 |
| DOI: |
10.32614/CRAN.package.ML2Pvae |
| Author: |
Geoffrey Converse [aut, cre, cph],
Suely Oliveira [ctb, ths],
Mariana Curi [ctb] |
| Maintainer: |
Geoffrey Converse <converseg at gmail.com> |
| License: |
MIT + file LICENSE |
| URL: |
https://converseg.github.io |
| NeedsCompilation: |
no |
| SystemRequirements: |
TensorFlow (https://www.tensorflow.org), Keras
(https://keras.io), TensorFlow Probability
(https://www.tensorflow.org/probability) |
| CRAN checks: |
ML2Pvae results |
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