Package: ML2Pvae 1.0.0.1

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.

Authors:Geoffrey Converse [aut, cre, cph], Suely Oliveira [ctb, ths], Mariana Curi [ctb]

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ML2Pvae.pdf |ML2Pvae.html✨
ML2Pvae/json (API)

# Install 'ML2Pvae' in R:
install.packages('ML2Pvae', repos = c('https://converseg.r-universe.dev', 'https://cloud.r-project.org'))

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Datasets:

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 4 scripts 175 downloads 5 exports 34 dependencies

Last updated 3 years agofrom:692b9d583c. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 27 2024
R-4.5-winNOTEOct 27 2024
R-4.5-linuxNOTEOct 27 2024
R-4.4-winNOTEOct 27 2024
R-4.4-macNOTEOct 27 2024
R-4.3-winNOTEOct 27 2024
R-4.3-macNOTEOct 27 2024

Exports:build_vae_correlatedbuild_vae_independentget_ability_parameter_estimatesget_item_parameter_estimatestrain_model

Dependencies:backportsbase64enccliconfiggenericsglueherejsonlitekeraslatticelifecyclemagrittrMatrixpngprocessxpsR6rappdirsRcppRcppTOMLreticulaterlangrprojrootrstudioapitensorflowtfautographtfprobabilitytfrunstidyselectvctrswhiskerwithryamlzeallot

ML2Pvae: Variational Autoencoder Models for IRT Parameter Estimation

Rendered fromml2p_vae_vignette.pdf.asisusingR.rsp::asison Oct 27 2024.

Last update: 2020-11-16
Started: 2020-11-16

Readme and manuals

Help Manual

Help pageTopics
Display a message upon loading package.onLoad
Build the encoder for a VAEbuild_hidden_encoder
Build a VAE that fits to a normal, full covariance N(m,S) latent distributionbuild_vae_correlated
Build a VAE that fits to a standard N(0,I) latent distribution with independent latent traitsbuild_vae_independent
Simulated latent abilities correlation matrixcorrelation_matrix
Simulated difficulty parametersdiff_true
Simulated discrimination parametersdisc_true
Feed forward response sets through the encoder, which outputs student ability estimatesget_ability_parameter_estimates
Get trainable variables from the decoder, which serve as item parameter estimates.get_item_parameter_estimates
ML2Pvae: A package for creating a VAE whose decoder recovers the parameters of the ML2P model. The encoder can be used to predict the latent skills based on assessment scores.ML2Pvae
A custom kernel constraint function that forces nonzero weights to be equal to one, so the VAE will estimate the 1-parameter logistic model. Nonzero weights are determined by the Q matrix.q_1pl_constraint
A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.q_constraint
Simulated Q-matrixq_matrix
Response dataresponses
A reparameterization in order to sample from the learned multivariate normal distribution of the VAEsampling_correlated
A reparameterization in order to sample from the learned standard normal distribution of the VAEsampling_independent
Simulated ability parameterstheta_true
Trains a VAE or autoencoder model. This acts as a wrapper for keras::fit().train_model
A custom loss function for a VAE learning a multivariate normal distribution with a full covariance matrixvae_loss_correlated
A custom loss function for a VAE learning a standard normal distributionvae_loss_independent
Give error messages for invalid inputs in exported functions.validate_inputs