├── .Rbuildignore ├── .Rhistory ├── .github ├── .gitignore └── workflows │ └── R-CMD-check.yaml ├── .gitignore ├── DESCRIPTION ├── LICENSE.txt ├── NAMESPACE ├── R ├── controls.R ├── cross-validation.R ├── data_handler.R ├── data_handler_torch.R ├── deep-ensembles.R ├── deepregression.R ├── deepregression_torch.R ├── dro.R ├── families.R ├── families_torch.R ├── formula_helpers.R ├── generator.R ├── helperfuns.R ├── layers.R ├── layers_torch.R ├── methods.R ├── models.R ├── nodelayer.R ├── orthogonalization.R ├── psplinelayer.R ├── psplinelayer_torch.R ├── special_processing.R ├── subnetwork_init.R ├── subnetwork_init_torch.R ├── tf_helpers.R ├── torch_helpers.R ├── utils-pipe.R └── zzz.R ├── README.md ├── codecov.yml ├── inst ├── CITATION └── python │ ├── activations │ ├── __init__.py │ └── sparsemax.py │ ├── common │ └── list_handling.py │ ├── distributions │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── mvr.cpython-310.pyc │ ├── mvr.py │ └── tweedie.py │ ├── generators │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── keras_generators.cpython-310.pyc │ ├── keras_generators.py │ └── rlayer.py │ ├── layers │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ ├── convlasso.cpython-310.pyc │ │ ├── lasso.cpython-310.pyc │ │ └── orthogonalization.cpython-310.pyc │ ├── bnlasso.py │ ├── convlasso.py │ ├── lasso.py │ ├── orthogonalization.py │ └── randomeffects.py │ ├── models │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ ├── custom_train_step.cpython-310.pyc │ │ └── model_trainable_para.cpython-310.pyc │ ├── custom_train_step.py │ ├── helpers.py │ └── model_trainable_para.py │ ├── node │ ├── __init__.py │ └── node.py │ ├── optimizers │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── discriminative_layer_training.cpython-310.pyc │ └── discriminative_layer_training.py │ ├── psplines │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── psplines.cpython-310.pyc │ └── psplines.py │ ├── tffuns │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── tffuns.cpython-310.pyc │ └── tffuns.py │ └── utils │ ├── __init__.py │ └── types.py ├── man ├── check_and_install.Rd ├── check_input_args_fit.Rd ├── choose_kernel_initializer_torch.Rd ├── coef.drEnsemble.Rd ├── collect_distribution_parameters.Rd ├── combine_penalties.Rd ├── convenience_layers.Rd ├── create_family.Rd ├── create_family_torch.Rd ├── create_penalty.Rd ├── cv.Rd ├── deepregression.Rd ├── distfun_to_dist.Rd ├── dr_families.Rd ├── ensemble.Rd ├── ensemble.deepregression.Rd ├── extract_S.Rd ├── extract_pure_gam_part.Rd ├── extractvar.Rd ├── family_to_tfd.Rd ├── family_to_trafo.Rd ├── family_to_trafo_torch.Rd ├── family_to_trochd.Rd ├── fitted.drEnsemble.Rd ├── form_control.Rd ├── formulaHelpers.Rd ├── from_dist_to_loss.Rd ├── from_dist_to_loss_torch.Rd ├── from_distfun_to_dist_torch.Rd ├── from_preds_to_dist.Rd ├── from_preds_to_dist_torch.Rd ├── gam_plot_data.Rd ├── get_distribution.Rd ├── get_ensemble_distribution.Rd ├── get_gam_part.Rd ├── get_gamdata.Rd ├── get_gamdata_reduced_nr.Rd ├── get_help_forward_torch.Rd ├── get_layer_by_opname.Rd ├── get_layernr_by_opname.Rd ├── get_layernr_trainable.Rd ├── get_luz_dataset.Rd ├── get_names_pfc.Rd ├── get_node_term.Rd ├── get_nodedata.Rd ├── get_partial_effect.Rd ├── get_processor_name.Rd ├── get_special.Rd ├── get_type_pfc.Rd ├── get_weight_by_name.Rd ├── get_weight_by_opname.Rd ├── hadamard_layers.Rd ├── hadamard_layers_torch.Rd ├── handle_gam_term.Rd ├── import_packages.Rd ├── import_tf_dependings.Rd ├── import_torch_dependings.Rd ├── keras_dr.Rd ├── layer_dense_module.Rd ├── layer_dense_torch.Rd ├── layer_node.Rd ├── layer_sparse_batch_normalization.Rd ├── layer_sparse_conv_2d.Rd ├── layer_spline.Rd ├── layer_spline_torch.Rd ├── log_score.Rd ├── loop_through_pfc_and_call_trafo.Rd ├── makeInputs.Rd ├── make_folds.Rd ├── make_generator.Rd ├── make_generator_from_matrix.Rd ├── makelayername.Rd ├── methodDR.Rd ├── model_torch.Rd ├── multioptimizer.Rd ├── na_omit_list.Rd ├── names_families.Rd ├── nn_init_no_grad_constant_deepreg.Rd ├── orthog_P.Rd ├── orthog_control.Rd ├── orthog_post_fitting.Rd ├── orthog_structured_smooths_Z.Rd ├── penalty_control.Rd ├── pipe.Rd ├── plot_cv.Rd ├── precalc_gam.Rd ├── predict_gam_handler.Rd ├── predict_gen.Rd ├── prepare_data.Rd ├── prepare_data_torch.Rd ├── prepare_input_list_model.Rd ├── prepare_newdata.Rd ├── prepare_torch_distr_mixdistr.Rd ├── process_terms.Rd ├── processors.Rd ├── quant.Rd ├── re_layers.Rd ├── reinit_weights.Rd ├── reinit_weights.deepregression.Rd ├── separate_define_relation.Rd ├── stddev.Rd ├── stop_iter_cv_result.Rd ├── subnetwork_init.Rd ├── subnetwork_init_torch.Rd ├── tf_repeat.Rd ├── tf_row_tensor.Rd ├── tf_split_multiple.Rd ├── tf_stride_cols.Rd ├── tf_stride_last_dim_tensor.Rd ├── tfd_mse.Rd ├── tfd_zinb.Rd ├── tfd_zip.Rd ├── torch_dr.Rd ├── update_miniconda_deepregression.Rd └── weight_control.Rd └── tests ├── testthat.R └── testthat ├── test_controls.R ├── test_customtraining.R ├── test_customtraining_torch.R ├── test_data_handler.R ├── test_data_handler_torch.R ├── test_deepregression.R ├── test_deepregression_torch.R ├── test_ensemble.R ├── test_ensemble_torch.R ├── test_families.R ├── test_families_torch.R ├── test_helperfuns.R ├── test_layers.R ├── test_layers_torch.R ├── test_methods.R ├── test_methods_torch.R ├── test_models.R ├── test_node.R ├── test_orthogonalization.R ├── test_orthogonalization_torch.R ├── test_reproducibility.R ├── test_reproducibility_torch.R ├── test_special_processing.R ├── test_special_processing_torch.R ├── test_subnetwork_init.R ├── test_subnetwork_init_torch.R ├── test_unstructured.R └── test_unstructured_torch.R /.Rbuildignore: -------------------------------------------------------------------------------- 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