DESCRIPTION

Package: deepregression
Title: Fitting Deep Distributional Regression
Version: 2.2.0
Authors@R: c(
    person("David", "Ruegamer", , "david.ruegamer@gmail.com", role = c("aut", "cre")),
    person("Christopher", "Marquardt", , "ch.marquardt@campus.lmu.de", role = c("ctb")),
    person("Laetitia", "Frost", , "lae.frost@campus.lmu.de ", role = c("ctb")),
    person("Florian", "Pfisterer", , "florian.pfisterer@stat.uni-muenchen.de", role = c("ctb")),
    person("Philipp", "Baumann", , "baumann@kof.ethz.ch", role = c("ctb")),
    person("Chris", "Kolb", , "chris.kolb@stat.uni-muenchen.de", role = c("ctb")),
    person("Lucas", "Kook", , "lucasheinrich.kook@uzh.ch", role = c("ctb")))
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.
Config/reticulate:
  list(
    packages = list(
      list(package = "six", pip = TRUE),
      list(package = "tensorflow", version = "2.15", pip = TRUE),
      list(package = "tensorflow_probability", version = "0.23", pip = TRUE),
      list(package = "keras", version = "2.15", pip = TRUE))
  )
Depends: 
    R (>= 4.0.0),
    tensorflow (>= 2.2.0),
    tfprobability,
    keras (>= 2.2.0)
Suggests:
    testthat,
    knitr,
    covr
Imports:
    mgcv,
    dplyr,
    R6,
    reticulate (>= 1.14),
    Matrix,
    magrittr,
    tfruns,
    methods,
    coro (>= 1.0.3),
    torchvision (>= 0.5.1),
    luz (>= 0.4.0),
    torch
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2