ReSurv/DESCRIPTION at main · edhofman/ReSurv · GitHub

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Package: ReSurv
Type: Package
Title: Machine Learning Models for Predicting Claim Counts
Version: 1.0.1
Authors@R:
    c(person(given = "Emil",
             family = "Hofman",
             role = c("aut", "cre", "cph"),
             email="emil_hofman@hotmail.dk"),
      person(given = "Gabriele",
             family = "Pittarello",
             role = c("aut", "cph"),
             email = "gabriele.pittarello@uniroma1.it",
             comment = c(ORCID = "0000-0003-3360-5826")),
      person(given = "Munir",
             family = "Hiabu",
             email="mh@math.ku.dk",
             role = c("aut", "cph"),
             comment = c(ORCID = "0000-0001-5846-667X")))
Description: Prediction of claim counts using the feature based development factors introduced in the manuscript Hiabu M., Hofman E. and Pittarello G. (2023) <doi:10.48550/arXiv.2312.14549>.
             Implementation of Neural Networks, Extreme Gradient Boosting,
             and Cox model with splines to optimise the partial log-likelihood of proportional hazard models.
URL: https://github.com/edhofman/ReSurv
BugReports: https://github.com/edhofman/ReSurv/issues
License: GPL (>= 2)
Depends: tidyverse
Imports:
  stats,
  dplyr,
  actuar,
  dtplyr,
  fastDummies,
  forecast,
  data.table,
  purrr,
  tidyr,
  tibble,
  ggplot2,
  survival,
  reshape2,
  bshazard,
  SynthETIC,
  rpart,
  reticulate,
  xgboost,
  SHAPforxgboost
SystemRequirements: Python (>= 3.8.0)
Encoding: UTF-8
LazyData: true
Suggests:
    knitr,
    rmarkdown,
    testthat (>= 3.0.0)
VignetteBuilder: knitr, rmarkdown
RoxygenNote: 7.3.2
Config/testthat/edition: 3