Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving
Overview
Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" doi:10.48550/arXiv.2206.08541.
- Initial CRAN submission.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-31
- ERROR13 OK, 1 ERROR2026-03-28
- OK14 OK2026-03-13
Show 2 earlier changesHide 2 earlier changes
- ERROR13 OK, 1 ERROR2026-03-12
- OK14 OK2026-03-10
Documentation
- Help pages
- 14
- Examples that run
- 100%13 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 29%
Downloads
Repository
Stars over time
Checks run against github.com/agi-lab/adlp on 2026-10-22.
No development-tooling practices detected in the upstream repository.
Dependencies
Installing ADLP adds no packages beyond those that come with R.
Code & Tests
Datasets
People & History
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.0Latest2024-04-18 · current release
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2024-04-18
- Total releases
- 1 / 2 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 112 KB / 2 files
- Download size
- 495 KB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("ADLP")The package provides a citation file.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.