combine_penalties

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

combine_penalties function

<dl><dt>penalties</dt>
<dd>a list of penalties</dd>
<dt>dims</dt>
<dd>dimensions of the parameters to penalize</dd></dl>

Arguments

Function to combine two penalties — combine_penalties

<dl>

<dt>penalties</dt>
<dd>a list of penalties</dd>


<dt>dims</dt>
<dd>dimensions of the parameters to penalize</dd>

</dl>

Function to combine two penalties

combine_penalties: Function to combine two penalties

Description

Usage

Value

Arguments