| .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. |