R: Fitting Deep Distributional Regression

# Fitting Deep Distributional Regression

## Documentation for package ‘deepregression’ version 1.0.0

- DESCRIPTION file.

## Help Pages

C D E F G H I K L M N O P Q R S T U W

## -- C --

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

## -- D --

deepregression | Fitting Semi-Structured Deep Distributional Regression
distfun_to_dist | Function to define output distribution based on dist_fun

## -- E --

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

## -- F --

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

## -- G --

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_names_pfc | Extract term names from the parsed formula content
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

## -- H --

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

## -- I --

int_processor | Function that creates layer for each processor
inverse_group_lasso_pen | Hadamard-type layers

## -- K --

keras_dr | Compile a Deep Distributional Regression Model

## -- L --

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

## -- M --

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

## -- N --

names_families | Returns the parameter names for a given family

## -- O --

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

## -- P --

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
predict_gen | Generator function for deepregression objects
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

## -- Q --

quant | Generic quantile function
quant.deepregression | Generic functions for deepregression models

## -- R --

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

## -- S --

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

## -- T --

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

## -- U --

update_miniconda_deepregression | Function to update miniconda and packages

## -- W --

weight_control | Options for weights of layers