Package: ReSurv 1.0.0

ReSurv: Machine Learning Models For Predicting Claim Counts

Prediction of claim counts using the feature based development factors introduced in the manuscript <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.

Authors:Emil Hofman [aut, cre, cph], Gabriele Pittarello [aut, cph], Munir Hiabu [aut, cph]

ReSurv_1.0.0.tar.gz
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ReSurv.pdf |ReSurv.html✨
ReSurv/json (API)

# Install 'ReSurv' in R:

install.packages('ReSurv', repos = c('https://edhofman.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/edhofman/resurv/issues

On CRAN:

5.67 score 1 stars 21 scripts 7 exports 180 dependencies

Last updated 9 days agofrom:4b03b27adb. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 15 2024
R-4.5-win	OK	Nov 15 2024
R-4.5-linux	OK	Nov 15 2024
R-4.4-win	OK	Nov 15 2024
R-4.4-mac	OK	Nov 15 2024
R-4.3-win	OK	Nov 15 2024
R-4.3-mac	OK	Nov 15 2024

Exports:data_generator IndividualDataPP install_pyresurv ooslkh ReSurv ReSurvCV survival_crps

Dependencies:abind askpass backports base64enc BBmisc bit bit64 blob boot broom bshazard bslib cachem callr car carData cellranger checkmate cli clipr cmprsk colorspace colourpicker commonmark conflicted corrplot cowplot cpp11 crayon curl data.table DBI dbplyr Deriv digest doBy dplyr dtplyr Epi etm evaluate fansi farver fastDummies fastmap fontawesome forcats forecast Formula fracdiff fs gargle generics ggExtra ggforce ggplot2 ggpubr ggrepel ggsci ggsignif glue googledrive googlesheets4 gridExtra gtable haven here highr hms htmltools htmlwidgets httpuv httr ids isoband jquerylib jsonlite knitr labeling later lattice lifecycle lme4 lmtest lubridate magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime miniUI minqa modelr munsell nlme nloptr nnet numDeriv openssl pbkrtest pillar pkgconfig plyr png polyclip polynom prettyunits processx progress promises ps purrr quadprog quantmod quantreg R6 ragg rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RcppTOML readr readxl rematch rematch2 reprex reshape2 reticulate rlang rmarkdown rpart rprojroot rstatix rstudioapi rvest sass scales selectr SHAPforxgboost shiny shinyjs sourcetools SparseM stringi stringr survival SynthETIC sys systemfonts textshaping tibble tidyr tidyselect tidyverse timechange timeDate tinytex tseries TTR tweenr tzdb urca utf8 uuid vctrs viridisLite vroom withr xfun xgboost xml2 xtable xts yaml zoo

A Machine Learning Approach Based On Survival Analysis For IBNR Frequencies In Non-Life Reserving

Gabriele Pittarello, Emil Hofman

Rendered fromManuscript_replication_material.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-12
Started: 2023-12-21

Claim Counts Prediction Using Individual Data

Gabriele Pittarello

Rendered fromcas_call.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-12
Started: 2024-06-29

Exploring The Variables Importance

Gabriele Pittarello

Rendered fromvariables_importance.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-12
Started: 2023-08-14

Hyperparameters Tuning

Gabriele Pittarello

Rendered fromhp_tuning.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-12
Started: 2023-06-23

Simulate Individual Data

Gabriele Pittarello

Rendered fromsimulate_individual_data.Rmdusingknitr::rmarkdownon Nov 15 2024.

Last update: 2024-11-12
Started: 2023-04-21

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Individual data generator	data_generator
Individual Data Pre-Processing	IndividualDataPP
Install Python Environment for ReSurv	install_pyresurv
Compute the out-of-sample likelihood	ooslkh
Compute the out-of-the sample likelihood	ooslkh.default
Compute the out-of-the sample likelihood	ooslkh.ReSurvFit
Helper functions	pkg.env
Plot for machine learning models	plot.ReSurvFit
Plot of the development factors	plot.ReSurvPredict
Predict IBNR frequency	predict.ReSurvFit
Print summary of IBNR predictions	print.summaryReSurvPredict
Fit 'ReSurv' models on the individual data.	ReSurv
Fit 'ReSurv' models on the individual data.	ReSurv.default
Fit 'ReSurv' models on the individual data.	ReSurv.IndividualDataPP
K fold cross-validation of a 'ReSurv' model.	ReSurvCV
K fold cross-validation of ReSurv model.	ReSurvCV.default
K fold cross-validation of ReSurv model.	ReSurvCV.IndividualDataPP
Summary of IBNR predictions	summary.ReSurvPredict
Survival continuously ranked probability score.	survival_crps
Survival continuously ranked probability score.	survival_crps.default
Survival continuously ranked probability score.	survival_crps.ReSurvFit

Package: ReSurv 1.0.0

ReSurv: Machine Learning Models For Predicting Claim Counts

A Machine Learning Approach Based On Survival Analysis For IBNR Frequencies In Non-Life Reserving

Claim Counts Prediction Using Individual Data

Exploring The Variables Importance

Hyperparameters Tuning

Simulate Individual Data

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)