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) . 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 Config/pak/sysreqs: make libicu-dev Repository: https://edhofman.r-universe.dev Date/Publication: 2026-09-15 08:18:47 UTC RemoteUrl: https://github.com/edhofman/resurv RemoteRef: HEAD RemoteSha: 17d9ef0995d83800d5553fe449e339b8857d5601 NeedsCompilation: no Packaged: 2026-10-15 09:10:31 UTC; root Author: Emil Hofman [aut, cre, cph], Gabriele Pittarello [aut, cph] (ORCID: ), Munir Hiabu [aut, cph] (ORCID: ) Maintainer: Emil Hofman