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deepregression  

Fitting Deep Distributional Regression
View on CRAN: Click here


Download and install deepregression package within the R console
Install from CRAN:
install.packages("deepregression")

Install from Github:
library("remotes")
install_github("cran/deepregression")

Install by package version:
library("remotes")
install_version("deepregression", "2.2.0")



Attach the package and use:
library("deepregression")
Maintained by
David Ruegamer
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-10-04
Latest Update: 2023-01-17
Description:
Allows for the specification of semi-structured deep distributional regression models which are fitted in a neural network as proposed by Ruegamer et al. (2023) . Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.
How to cite:
David Ruegamer (2021). deepregression: Fitting Deep Distributional Regression. R package version 2.2.0, https://cran.r-project.org/web/packages/deepregression. Accessed 22 Dec. 2024.
Previous versions and publish date:
0.1 (2021-10-04 21:50), 0.2 (2022-09-21 11:00), 0.3.1 (2022-11-24 10:00), 0.3 (2022-11-16 10:00), 1.0.0 (2023-01-17 23:50)
Other packages that cited deepregression R package
View deepregression citation profile
Other R packages that deepregression depends, imports, suggests or enhances
Complete documentation for deepregression
Functions, R codes and Examples using the deepregression R package
Some associated functions: check_and_install . coef.drEnsemble . combine_penalties . convenience_layers . create_family . create_penalty . cv . deepregression . distfun_to_dist . dr_families . ensemble.deepregression . ensemble . extract_S . extract_pure_gam_part . extractvar . family_to_tfd . family_to_trafo . fitted.drEnsemble . form_control . formulaHelpers . from_dist_to_loss . from_preds_to_dist . gam_plot_data . get_distribution . get_ensemble_distribution . get_gam_part . get_gamdata . get_gamdata_reduced_nr . get_layer_by_opname . get_layernr_by_opname . get_layernr_trainable . get_names_pfc . get_partial_effect . get_processor_name . get_special . get_type_pfc . get_weight_by_name . get_weight_by_opname . hadamard_layers . handle_gam_term . keras_dr . layer_sparse_conv_2d . layer_spline . log_score . loop_through_pfc_and_call_trafo . makeInputs . make_folds . make_generator . make_generator_from_matrix . makelayername . methodDR . multioptimizer . names_families . orthog_P . orthog_control . orthog_post_fitting . orthog_structured_smooths_Z . penalty_control . pipe . plot_cv . precalc_gam . predict_gen . prepare_data . prepare_newdata . process_terms . processors . quant . reinit_weights.deepregression . reinit_weights . separate_define_relation . stddev . stop_iter_cv_result . subnetwork_init . tf_repeat . tf_row_tensor . tf_split_multiple . tf_stride_cols . tf_stride_last_dim_tensor . tfd_mse . tfd_zinb . tfd_zip . update_miniconda_deepregression . weight_control . 
Some associated R codes: aaa.R . controls.R . cross-validation.R . data_handler.R . deep-ensembles.R . deepregression.R . dro.R . families.R . formula_helpers.R . generator.R . helperfuns.R . layers.R . methods.R . models.R . orthogonalization.R . psplinelayer.R . special_processing.R . subnetwork_init.R . tf_helpers.R . utils-pipe.R . zzz.R .  Full deepregression package functions and examples
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