R: Variational Autoencoder Models for IRT Parameter Estimation

# Variational Autoencoder Models for IRT Parameter Estimation

## Documentation for package ‘ML2Pvae’ version 1.0.0.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

.onLoad | Display a message upon loading package
build_hidden_encoder | Build the encoder for a VAE
build_vae_correlated | Build a VAE that fits to a normal, full covariance N(m,S) latent distribution
build_vae_independent | Build a VAE that fits to a standard N(0,I) latent distribution with independent latent traits
correlation_matrix | Simulated latent abilities correlation matrix
diff_true | Simulated difficulty parameters
disc_true | Simulated discrimination parameters
get_ability_parameter_estimates | Feed forward response sets through the encoder, which outputs student ability estimates
get_item_parameter_estimates | Get trainable variables from the decoder, which serve as item parameter estimates.
ML2Pvae | 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.
q_1pl_constraint | 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_constraint | A custom kernel constraint function that restricts weights between the learned distribution and output. Nonzero weights are determined by the Q matrix.
q_matrix | Simulated Q-matrix
responses | Response data
sampling_correlated | A reparameterization in order to sample from the learned multivariate normal distribution of the VAE
sampling_independent | A reparameterization in order to sample from the learned standard normal distribution of the VAE
theta_true | Simulated ability parameters
train_model | Trains a VAE or autoencoder model. This acts as a wrapper for keras::fit().
vae_loss_correlated | A custom loss function for a VAE learning a multivariate normal distribution with a full covariance matrix
vae_loss_independent | A custom loss function for a VAE learning a standard normal distribution
validate_inputs | Give error messages for invalid inputs in exported functions.