_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-deepregression 2.3.2
Propagated dependencies: r-torchvision@0.8.0 r-torch@0.16.3 r-tfruns@1.5.4 r-tfprobability@0.15.2 r-tensorflow@2.20.0 r-reticulate@1.44.1 r-r6@2.6.1 r-mgcv@1.9-4 r-matrix@1.7-4 r-magrittr@2.0.4 r-luz@0.5.1 r-keras@2.16.0 r-dplyr@1.1.4 r-coro@1.1.0
Channel: guix-cran
Location: guix-cran/packages/d.scm (guix-cran packages d)
Home page: https://cran.r-project.org/package=deepregression
Licenses: GPL 3
Synopsis: Fitting Deep Distributional Regression
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) <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.

Total results: 1