Package: ReSurv
Type: Package
Depends: R (>= 4.1.0)
Title: Machine Learning Models for Predicting Claim Counts
Version: 1.1.0
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,
        https://edhofman.github.io/ReSurv/
BugReports: https://github.com/edhofman/ReSurv/issues
License: GPL (>= 2)
Imports: stats, dplyr (>= 1.1.0), actuar, fastDummies, data.table,
        purrr, tidyr, ggplot2, lubridate, survival, SynthETIC, xgboost
Encoding: UTF-8
Suggests: bshazard, clmplus, knitr, torch, rmarkdown, rpart, testthat
        (>= 3.0.0)
VignetteBuilder: knitr, rmarkdown
RoxygenNote: 7.3.3
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2026-09-15 08:29:57 UTC; pwt887
Author: Emil Hofman [aut, cre, cph],
  Gabriele Pittarello [aut, cph] (ORCID:
    <https://orcid.org/0000-0003-3360-5826>),
  Munir Hiabu [aut, cph] (ORCID: <https://orcid.org/0000-0001-5846-667X>)
Maintainer: Emil Hofman <emil_hofman@hotmail.dk>
Repository: CRAN
Date/Publication: 2026-09-15 08:50:02 UTC
