.onLoad                 Display a message upon loading package
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.
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.
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.
