Fitting Deep Distributional Regression


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Documentation for package ‘deepregression’ version 0.1

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check_and_install Function to check python environment and install necessary packages
coef.deepregression Generic functions for deepregression models
create_family Function to create (custom) family
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
extractval Extract value in term name
family_to_tfd Character-tfd mapping function
family_to_trafo Character-to-transformation mapping function
fit Generic train function
fit.deepregression Generic functions for deepregression models
fitted.deepregression Generic functions for deepregression models
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
get_distribution Function to return the fitted distribution
get_partial_effect Return partial effect of one smooth term
get_type_pfc Function to subset parsed formulas
get_weight_by_name Function to retrieve the weights of a structured layer
handle_gam_term Function to define smoothness and call mgcv's smooth constructor
keras_dr Compile a Deep Distributional Regression Model
layer_add_identity Convenience layer function
layer_concatenate_identity Convenience layer function
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
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
names_families Returns the parameter names for a given family
orthog_control Options for orthogonalization
penalty_control Options for penalty setup in the pre-processing
plot.deepregression Generic functions for deepregression models
plot_cv Plot CV results from deepregression
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
processor Control function to define the processor for terms in the formula
quant Generic quantile function
quant.deepregression Generic functions for deepregression models
separate_define_relation Function to define orthogonalization connections in the formula
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_zinb Implementation of a zero-inflated negbinom distribution for TFP
tfd_zip Implementation of a zero-inflated poisson distribution for TFP
tf_stride_cols Function to index tensors columns