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.
- Version1.0.0.1
- R versionunknown
- LicenseMIT
- Needs compilation?No
- Last release05/23/2022
Documentation
Team
Geoffrey Converse
Suely Oliveira
Show author detailsRolesContributor, Thesis advisorMariana Curi
Show author detailsRolesContributor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports4 packages
- Suggests4 packages