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.

Getting started

Package details

AuthorGeoffrey Converse [aut, cre, cph], Suely Oliveira [ctb, ths], Mariana Curi [ctb]
MaintainerGeoffrey Converse <converseg@gmail.com>
LicenseMIT + file LICENSE
Version1.0.0.1
URL https://converseg.github.io
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("ML2Pvae")

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ML2Pvae documentation built on May 23, 2022, 9:05 a.m.