SPLICE
An individual claim simulator of Synthetic Paid Loss and Incurred Cost Experience (SPLICE)
Science Score: 36.0%
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Low similarity (12.8%) to scientific vocabulary
Repository
An individual claim simulator of Synthetic Paid Loss and Incurred Cost Experience (SPLICE)
Basic Info
Statistics
- Stars: 6
- Watchers: 0
- Forks: 2
- Open Issues: 2
- Releases: 1
Metadata Files
README.md
SPLICE

Introduction
We introduce SPLICE (Synthetic Paid Loss and Incurred Cost Experience), an extension to the individual claim simulator called SynthETIC. SPLICE serves to simulate the evolution of case estimates of incurred losses through the lifetime of a claim, under a flexible modelling framework which resembles that of SynthETIC.
An initial set of test parameters, designed to mirror the experience of a real insurance portfolio, were set up and applied by default to generate a realistic test data set of incurred histories (see vignette). However, the distributional assumptions used to generate this data set can be easily modified by users to match their experiences.
Reference
For a full description of SPLICE's structure and test parameters, readers should refer to:
Avanzi, B., Taylor, G., Wang, M., 2023. SPLICE: A Synthetic Paid Loss and Incurred Cost Experience Simulator. Annals of Actuarial Science 17(1), 7–35. https://doi.org/10.1017/S1748499522000057
A free copy is accessible via arXiv:2109.04058.
To cite this package in publications, please use:
citation("SPLICE")
Install Package
To install the CRAN version of the package, do
install.packages("SPLICE")
To install the development version of the package from this GitHub repository, do
if (!require(remotes)) install.packages("remotes")
remotes::install_github("agi-lab/SPLICE/SPLICE", build_vignettes = TRUE)
After the installation, run
library(SPLICE)
as you would normally do will load the package. View a full demonstration of the package (which is used to generate the built-in test datasets discussed in the paper) by running
vignette("SPLICE-demo", package = "SPLICE")
Additional Resources
Owner
- Name: Australian General Insurance Lab (AGIL)
- Login: agi-lab
- Kind: organization
- Repositories: 3
- Profile: https://github.com/agi-lab
This lab intends to make research made by Australian general insurance researchers available to the public.
GitHub Events
Total
- Watch event: 1
Last Year
- Watch event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Melantha Wang | w****a@g****m | 46 |
| melantha-wang | 5****g | 10 |
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 1
- Total pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 7 days
- Total issue authors: 1
- Total pull request authors: 3
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- melantha-wang (1)
Pull Request Authors
- Pat-Laub (2)
- yunwaiho (2)
- melantha-wang (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- cran 243 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 4
- Total maintainers: 1
cran.r-project.org: SPLICE
Synthetic Paid Loss and Incurred Cost Experience (SPLICE) Simulator
- Homepage: https://github.com/agi-lab/SPLICE
- Documentation: http://cran.r-project.org/web/packages/SPLICE/SPLICE.pdf
- License: GPL-3
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Latest release: 1.1.2
published over 2 years ago
Rankings
Maintainers (1)
Dependencies
- R >= 2.10 depends
- SynthETIC >= 1.0.0 depends
- lifecycle * imports
- methods * imports
- stats * imports
- zoo * imports
- ChainLadder * suggests
- RColorBrewer * suggests
- actuar * suggests
- dplyr * suggests
- knitr * suggests
- rmarkdown * suggests