Man pages for deepregression
Fitting Deep Distributional Regression
| check_and_install | Function to check python environment and install necessary... |
| check_input_args_fit | Function to check if inputs are supported by corresponding... |
| choose_kernel_initializer_torch | Function to choose a kernel initializer for a torch layer |
| coef.drEnsemble | Method for extracting ensemble coefficient estimates |
| collect_distribution_parameters | Character-to-parameter collection function needed for mixture... |
| combine_penalties | Function to combine two penalties |
| convenience_layers | Convenience layer function |
| create_family | Function to create (custom) family |
| create_family_torch | Function to create (custom) family |
| create_penalty | Function to create mgcv-type penalty |
| cv | Generic cv function |
| deepregression | Fitting Semi-Structured Deep Distributional Regression |
| distfun_to_dist | Function to define output distribution based on dist_fun |
| dr_families | Families for deepregression |
| ensemble | Generic deep ensemble function |
| ensemble.deepregression | Ensembling deepregression models |
| extract_pure_gam_part | Extract the smooth term from a deepregression term... |
| extract_S | Convenience function to extract penalty matrix and value |
| extractvar | Extract variable from term |
| family_to_tfd | Character-tfd mapping function |
| family_to_trafo | Character-to-transformation mapping function |
| family_to_trafo_torch | Character-to-transformation mapping function |
| family_to_trochd | Character-torch mapping function |
| fitted.drEnsemble | Method for extracting the fitted values of an ensemble |
| form_control | Options for formula parsing |
| formulaHelpers | Formula helpers |
| from_distfun_to_dist_torch | Function to define output distribution based on dist_fun |
| from_dist_to_loss | Function to transform a distritbution layer output into a... |
| from_dist_to_loss_torch | Function to transform a distribution layer output into a loss... |
| from_preds_to_dist | Define Predictor of a Deep Distributional Regression Model |
| from_preds_to_dist_torch | Define Predictor of a Deep Distributional Regression Model |
| gam_plot_data | used by gam_processor |
| get_distribution | Function to return the fitted distribution |
| get_ensemble_distribution | Obtain the conditional ensemble distribution |
| get_gamdata | Extract property of gamdata |
| get_gamdata_reduced_nr | Extract number in matching table of reduced gam term |
| get_gam_part | Extract gam part from wrapped term |
| get_help_forward_torch | Helper function to calculate amount of layers Needed when... |
| get_layer_by_opname | Function to return layer given model and name |
| get_layernr_by_opname | Function to return layer number given model and name |
| get_layernr_trainable | Function to return layer numbers with trainable weights |
| get_luz_dataset | Helper function to create an function that generates R6... |
| get_names_pfc | Extract term names from the parsed formula content |
| get_nodedata | Extract attributes/hyper-parameters of the node term |
| get_node_term | Extract variables from wrapped node term |
| get_partial_effect | Return partial effect of one smooth term |
| get_processor_name | Extract processor name from term |
| get_special | Extract terms defined by specials in formula |
| get_type_pfc | Function to subset parsed formulas |
| get_weight_by_name | Function to retrieve the weights of a structured layer |
| get_weight_by_opname | Function to return weight given model and name |
| hadamard_layers | Hadamard-type layers |
| hadamard_layers_torch | Hadamard-type layers torch |
| handle_gam_term | Function to define smoothness and call mgcv's smooth... |
| import_packages | Function to import required packages |
| import_tf_dependings | Function to import required packages for tensorflow @import... |
| import_torch_dependings | Function to import required packages for torch @import torch... |
| keras_dr | Compile a Deep Distributional Regression Model |
| layer_dense_module | Function to create custom nn_linear module to overwrite... |
| layer_dense_torch | Function to define a torch layer similar to a tf dense layer |
| layer_node | NODE/ODTs Layer |
| layer_sparse_batch_normalization | Sparse Batch Normalization layer |
| layer_sparse_conv_2d | Sparse 2D Convolutional layer |
| layer_spline | Function to define spline as TensorFlow layer |
| layer_spline_torch | Function to define spline as Torch layer |
| log_score | Function to return the log_score |
| loop_through_pfc_and_call_trafo | Function to loop through parsed formulas and apply data trafo |
| make_folds | Generate folds for CV out of one hot encoded matrix |
| make_generator | creates a generator for training |
| make_generator_from_matrix | Make a DataGenerator from a data.frame or matrix |
| makeInputs | Convenience layer function |
| makelayername | Function that takes term and create layer name |
| methodDR | Generic functions for deepregression models |
| model_torch | Function to initialize a nn_module Forward functions works... |
| multioptimizer | Function to define an optimizer combining multiple optimizers |
| names_families | Returns the parameter names for a given family |
| na_omit_list | Function to exclude NA values |
| nn_init_no_grad_constant_deepreg | custom nn_linear module to overwrite reset_parameters #... |
| orthog_control | Options for orthogonalization |
| orthog_P | Function to compute adjusted penalty when orthogonalizing |
| orthog_post_fitting | Orthogonalize a Semi-Structured Model Post-hoc |
| orthog_structured_smooths_Z | Orthogonalize structured term by another matrix |
| penalty_control | Options for penalty setup in the pre-processing |
| pipe | Pipe operator |
| plot_cv | Plot CV results from deepregression |
| precalc_gam | Pre-calculate all gam parts from the list of formulas |
| predict_gam_handler | Handler for prediction with gam terms |
| predict_gen | Generator function for deepregression objects |
| prepare_data | Function to prepare data based on parsed formulas |
| prepare_data_torch | Function to additionally prepare data for fit process (torch) |
| prepare_input_list_model | Function to prepare input list for fit process, due to... |
| prepare_newdata | Function to prepare new data based on parsed formulas |
| prepare_torch_distr_mixdistr | Prepares distributions for mixture process |
| processors | Function that creates layer for each processor |
| process_terms | Control function to define the processor for terms in the... |
| quant | Generic quantile function |
| reinit_weights | Generic function to re-intialize model weights |
| reinit_weights.deepregression | Method to re-initialize weights of a '"deepregression"' model |
| re_layers | random effect layer |
| separate_define_relation | Function to define orthogonalization connections in the... |
| stddev | Generic sd function |
| stop_iter_cv_result | Function to get the stoppting iteration from CV |
| subnetwork_init | Initializes a Subnetwork based on the Processed Additive... |
| subnetwork_init_torch | Initializes a Subnetwork based on the Processed Additive... |
| tfd_mse | For using mean squared error via TFP |
| tfd_zinb | Implementation of a zero-inflated negbinom distribution for... |
| tfd_zip | Implementation of a zero-inflated poisson distribution for... |
| tf_repeat | TensorFlow repeat function which is not available for TF 2.0 |
| tf_row_tensor | Row-wise tensor product using TensorFlow |
| tf_split_multiple | Split tensor in multiple parts |
| tf_stride_cols | Function to index tensors columns |
| tf_stride_last_dim_tensor | Function to index tensors last dimension |
| torch_dr | Compile a Deep Distributional Regression Model (Torch) |
| update_miniconda_deepregression | Function to update miniconda and packages |
| weight_control | Options for weights of layers |
