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ML2Pvae  

Variational Autoencoder Models for IRT Parameter Estimation
View on CRAN: Click here


Download and install ML2Pvae package within the R console
Install from CRAN:
install.packages("ML2Pvae")

Install from Github:
library("remotes")
install_github("cran/ML2Pvae")

Install by package version:
library("remotes")
install_version("ML2Pvae", "1.0.0.1")



Attach the package and use:
library("ML2Pvae")
Maintained by
Geoffrey Converse
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2020-11-16
Latest Update: 2022-05-23
Description:
Based on the work of Curi, Converse, Hajewski, and Oliveira (2019) . 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.
How to cite:
Geoffrey Converse (2020). ML2Pvae: Variational Autoencoder Models for IRT Parameter Estimation. R package version 1.0.0.1, https://cran.r-project.org/web/packages/ML2Pvae. Accessed 06 Mar. 2026.
Previous versions and publish date:
1.0.0 (2020-11-16 10:10)
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Complete documentation for ML2Pvae
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