Fitting Deep Distributional Regression

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Documentation for package ‘deepregression’ version 0.2

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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