Fitting Deep Distributional Regression

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Documentation for package ‘deepregression’ version 2.2.0

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

-- A --

autogam_processor Function that creates layer for each processor

-- C --

check_and_install Function to check python environment and install necessary packages
check_input_args_fit Function to check if inputs are supported by corresponding fit function
choose_kernel_initializer_torch Function to choose a kernel initializer for a torch layer
coef.deepregression Generic functions for deepregression models
coef.drEnsemble Method for extracting ensemble coefficient estimates
collect_distribution_parameters Character-to-parameter collection function needed for mixture of same distribution (torch)
combine_penalties Function to combine two penalties
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
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 Ensembling deepregression models
extractlen Formula helpers
extractval Formula helpers
extractvals 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
family_to_trafo_torch Character-to-transformation mapping function
family_to_trochd Character-torch 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_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 loss function
from_dist_to_loss_torch Function to transform a distribution layer output into a loss function
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

-- 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_help_forward_torch Helper function to calculate amount of layers Needed when shared layers are used, because of layers have same names
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_luz_dataset Helper function to create an function that generates R6 instances of class dataset
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

-- H --

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

-- I --

import_packages Function to import required packages
import_tf_dependings Function to import required packages for tensorflow @import tensorflow tfprobability keras
import_torch_dependings Function to import required packages for torch @import torch torchvision luz
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_dense_module Function to create custom nn_linear module to overwrite reset_parameters
layer_dense_torch Function to define a torch layer similar to a tf dense layer
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_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
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
make_torch_dist Families for deepregression
mean.deepregression Generic functions for deepregression models
model_torch Function to initialize a nn_module Forward functions works with a list. The entries of the list are the input of the subnetworks
multioptimizer Function to define an optimizer combining multiple optimizers

-- N --

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 # nn_init_constant works only if value is scalar; so warmstarts for gam does'not work
node_processor Function that creates layer for each processor

-- 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
pen_layer random effect layer
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_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 different approaches
prepare_newdata Function to prepare new data based on parsed formulas
prepare_torch_distr_mixdistr Prepares distributions for mixture process
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 Generic function to re-intialize model weights
reinit_weights.deepregression Method to re-initialize weights of a '"deepregression"' model
re_layer random effect layer
ri_processor Function that creates layer for each processor

-- S --

separate_define_relation Function to define orthogonalization connections in the formula
simplyconnected_layer Hadamard-type layers
simplyconnected_layer_torch Hadamard-type layers torch
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
subnetwork_init_torch 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
tibgroup_layer_torch Hadamard-type layers torch
tiblinlasso_layer_torch Hadamard-type layers torch
tib_layer Hadamard-type layers
tib_layer_torch Hadamard-type layers torch
torch_dr Compile a Deep Distributional Regression Model (Torch)

-- U --

update_miniconda_deepregression Function to update miniconda and packages

-- W --

weight_control Options for weights of layers