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DESCRIPTION
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DESCRIPTION
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Package: ReSurv
Type: Package
Title: Machine learning based models for predicting IBNR frequency.
Version: 0.0.2
Authors@R:
c(person(given = "Emil",
family = "Hofman",
role = c("aut", "cre", "cph"),
email="[email protected]"),
person(given = "Gabriele",
family = "Pittarello",
role = c("aut", "cph"),
email = "[email protected]",
comment = c(ORCID = "0000-0003-3360-5826")),
person(given = "Munir",
family = "Hiabu",
email="[email protected]",
role = c("aut", "cph"),
comment = c(ORCID = "0000-0001-5846-667X")))
Description: Prediction of future IBNR frequencies using the feature based development factors introduced in Hiabu, Hofman, Pittarello (2023) <doi:10.48550/arXiv.2312.14549>.
Implementation of Neural Networks (NN), eXtreme Gradient Boosting (XGB),
and Cox model with splines (COX) 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,
lubridate,
dplyr,
dtplyr,
fastDummies,
forecast,
data.table,
reshape,
purrr,
survival,
LTRCtrees,
reshape2,
bshazard,
SynthETIC,
rpart,
reticulate,
xgboost,
SHAPforxgboost
Encoding: UTF-8
LazyData: true
Suggests:
knitr,
rmarkdown
VignetteBuilder: knitr, rmarkdown
RoxygenNote: 7.2.3