create_family_torch

Function to create (custom) family

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) <doi:10.18637/jss.v105.i02>.
Predictors can be modeled using structured (penalized) linear effects, structured non-linear effects or using an unstructured deep network model.

David Ruegamer

deepregression

Fitting Deep Distributional Regression

Christopher Marquardt

Laetitia Frost

Florian Pfisterer

Philipp Baumann

Chris Kolb

Lucas Kook

create_family_torch function

<dl><dt>torch_dist</dt>
<dd>a torch probability distribution</dd>
<dt>trafo_list</dt>
<dd>list of transformations h for each parameter 
(e.g, <code>exp</code> for a variance parameter)</dd>
<dt>output_dim</dt>
<dd>integer defining the size of the response</dd></dl>

Arguments

Function to create (custom) family — create_family_torch

<dl>

<dt>torch_dist</dt>
<dd>a torch probability distribution</dd>


<dt>trafo_list</dt>
<dd>list of transformations h for each parameter 
(e.g, <code>exp</code> for a variance parameter)</dd>


<dt>output_dim</dt>
<dd>integer defining the size of the response</dd>

</dl>

create_family_torch: Function to create (custom) family

Description

Usage

Value

Arguments