Man pages for enweg/quickbnnr
Quick way to implement Bayesian Neural Networks in R and estimating these using Julia's Turing
| BDense | Create a Dense layer that has a standard normal prior for... |
| BNN | Create the actual BNN model |
| BRNN | Adds a Baysian RNN layer to the network |
| Chain | Create a network by chaining layers |
| clean_parameters | Clean parameters for better understanding |
| DenseForcePosFirstWeight | Create a layer that enforces positive weights in the first... |
| DenseOrderedBias | Create a Dense layer with ordered biases |
| DenseOrderedWeights | Create a dense layer with ordered first weight column |
| dot-summary | Just a helper function |
| dot-tensor_embed | helper function |
| estimate | Estimating a BNN model using the NUTS default sampler in... |
| get_random_symbol | Creates a random string that is used as variable in julia |
| make_net | Create a BNN based on a specification |
| ndims | helper function to determine dimensions of object |
| predict.quickbnnr.estimate | Uses posterior draws for prediction |
| quickbnnr_seed | Sets a seed for replication purposes |
| quickbnnr_setup | Set up of the Julia environment needed for QuickBNN |
| summary.quickbnnr.estimate | Returns summary statistics of an estimated model |
| tensor_embed | Make a tensor of sequences |
