check_and_install | Function to check python environment and install necessary packages |

coef.deepregression | Generic functions for deepregression models |

coef.drEnsemble | Method for extracting ensemble coefficient estimates |

combine_penalties | Function to combine two penalties |

create_family | Function to create (custom) family |

create_penalty | Function to create mgcv-type penalty |

cv | Generic cv function |

cv.deepregression | Generic functions for deepregression models |

deepregression | Fitting Semi-Structured Deep Distributional Regression |

distfun_to_dist | Function to define output distribution based on dist_fun |

ensemble | Generic deep ensemble function |

ensemble.deepregression | Ensemblind deepregression models |

extractlen | Formula helpers |

extractval | Formula helpers |

extractvar | Extract variable from term |

extract_pure_gam_part | Extract the smooth term from a deepregression term specification |

extract_S | Convenience function to extract penalty matrix and value |

family_to_tfd | Character-tfd mapping function |

family_to_trafo | Character-to-transformation mapping function |

fit.deepregression | Generic functions for deepregression models |

fitted.deepregression | Generic functions for deepregression models |

fitted.drEnsemble | Method for extracting the fitted values of an ensemble |

form2text | Formula helpers |

form_control | Options for formula parsing |

from_dist_to_loss | Function to transform a distritbution layer output into a loss function |

from_preds_to_dist | Define Predictor of a Deep Distributional Regression Model |

gam_plot_data | used by gam_processor |

gam_processor | Function that creates layer for each 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_layernr_by_opname | Function to return layer number given model and name |

get_layernr_trainable | Function to return layer numbers with trainable weights |

get_layer_by_opname | Function to return layer given model and name |

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 |

handle_gam_term | Function to define smoothness and call mgcv's smooth constructor |

int_processor | Function that creates layer for each processor |

inverse_group_lasso_pen | Hadamard-type layers |

keras_dr | Compile a Deep Distributional Regression Model |

layer_add_identity | Convenience layer function |

layer_concatenate_identity | Convenience layer function |

layer_generator | Function that creates layer for each processor |

layer_group_hadamard | Hadamard-type layers |

layer_hadamard | Hadamard-type layers |

layer_hadamard_diff | Hadamard-type layers |

layer_sparse_conv_2d | Sparse 2D Convolutional layer |

layer_spline | Function to define spline as TensorFlow layer |

lin_processor | Function that creates layer for each processor |

log_score | Function to return the log_score |

loop_through_pfc_and_call_trafo | Function to loop through parsed formulas and apply data trafo |

makeInputs | Convenience layer function |

makelayername | Function that takes term and create layer name |

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 |

make_tfd_dist | Families for deepregression |

mean.deepregression | Generic functions for deepregression models |

multioptimizer | Function to define an optimizer combining multiple optimizers |

names_families | Returns the parameter names for a given family |

orthog_control | Options for orthogonalization |

orthog_P | Function to compute adjusted penalty when orthogonalizing |

orthog_structured_smooths_Z | Orthogonalize structured term by another matrix |

penalty_control | Options for penalty setup in the pre-processing |

plot.deepregression | Generic functions for deepregression models |

plot_cv | Plot CV results from deepregression |

precalc_gam | Pre-calculate all gam parts from the list of formulas |

predict.deepregression | Generic functions for deepregression models |

prepare_data | Function to prepare data based on parsed formulas |

prepare_newdata | Function to prepare new data based on parsed formulas |

print.deepregression | Generic functions for deepregression models |

process_terms | Control function to define the processor for terms in the formula |

quant | Generic quantile function |

quant.deepregression | Generic functions for deepregression models |

regularizer_group_lasso | Hadamard-type layers |

reinit_weights | Genereic function to re-intialize model weights |

reinit_weights.deepregression | Method to re-initialize weights of a '"deepregression"' model |

separate_define_relation | Function to define orthogonalization connections in the formula |

simplyconnected_layer | Hadamard-type layers |

stddev | Generic sd function |

stddev.deepregression | Generic functions for deepregression models |

stop_iter_cv_result | Function to get the stoppting iteration from CV |

subnetwork_init | Initializes a Subnetwork based on the Processed Additive Predictor |

tfd_mse | For using mean squared error via TFP |

tfd_zinb | Implementation of a zero-inflated negbinom distribution for TFP |

tfd_zip | Implementation of a zero-inflated poisson distribution for TFP |

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 |

tibgroup_layer | Hadamard-type layers |

tib_layer | Hadamard-type layers |

weight_control | Options for weights of layers |