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# clmplus

`clmplus` is an `R` package for implementing the age-period-cohort models for the claim development presented in the manuscript 'Replicating and extending chain-ladder via an age-period-cohort structure on the claim development in a run-off triangle' <doi:10.1080/10920277.2025.2496725>. 

## Our models

The models for the claim development available in the `clmplus` are based on the models for human mortality implemented in the `StMoMo` package. Users can either rely on our default models or set their own configuration for the claim development.


|      Model      | Lexis dimension                 |Claims reserving                  |
| :-------------: |:-------------------------------:|---------------------------------:|
| a               | age                             |development (chain-ladder model)  |
| ac              | age-cohort                      |development-accident              |
| ap              | age-period                      |development-calendar              |
| apc             | age-period-cohort               |development-calendar-accident     |

## Installation 

The developer version of `clmplus` can be installed from GitHub.

```
library(devtools)
devtools::install_github("gpitt71/clmplus")

```
The current version of `clmplus` can be installed from CRAN.

```
install.packages('clmplus')

```

## Get Started

In this brief example, we work with the `sifa.mtpl` data from the `clmplus` package. Further examples can be found in the [package vignettes](https://github.com/gpitt71/clmplus/tree/main/vignettes). The data set of cumulative claim payments is transformed into an `AggregateDataPP` object that pre-processes the data for claim development modelling.

```
library(clmplus)

data ("sifa.mtpl")
dataset = sifa.mtpl
datapp = AggregateDataPP(cumulative.payments.triangle = dataset, eta= 1/2)
```

Our models can be fit with the `clmplus` function.

```
a.model.fit=clmplus(datapp,
                 hazard.model = "a") # age-model replicates the chain ladder
                 
ac.model.fit=clmplus(datapp,
                 hazard.model = "ac")

ap.model.fit=clmplus(datapp,
                 hazard.model = "ap")

apc.model.fit=clmplus(datapp,
                  hazard.model = "apc")

```

The `plot` function can be be used to explore the scaled deviance residuals of fitted models. Below, an example for the age-period-cohort (`apc`) model for the claim development. 

```
plot(apc.model.fit)
```

Predictions are performed with the `predict` function.

```
a.model=predict(a.model.fit)
                 
# clmplus reserve (age model)
sum(a.model$reserve)
#226875.5


ac.model=predict(ac.model.fit,
                 gk.fc.model = 'a',
                 gk.order = c(1,1,0))
                 
# clmplus reserve (age-cohort model)
sum(ac.model$reserve)
#205305.7

ap.model= predict(ap.model.fit,
                 ckj.fc.model = 'a',
                 ckj.order = c(0,1,0))

# clmplus reserve (age-period model)
sum(ap.model$reserve)
#215602.8
          
                 
apc.model= predict(apc.model.fit,
                  gk.fc.model = 'a',
                  ckj.fc.model = 'a',
                  gk.order = c(1,1,0),
                  ckj.order = c(0,1,0))
# clmplus reserve (age-period-cohort model)
sum(apc.model$reserve)
#213821.6
```

The fitted effect (and extrapolated) effects can be inspected with the `plot` function. We continue below the example with the `apc` model.

```
plot(apc.model)
```

## Citation

**APA Style**  

Pittarello, G., Hiabu, M., & Villegas, A. M. (2025). Replicating and Extending Chain-Ladder via an Age–Period–Cohort Structure on the Claim Development in a Run-Off Triangle. North American Actuarial Journal, 1-31.

**BibTeX**  
```bibtex
@article{ ,
  title={Replicating and Extending Chain-Ladder via an Age--Period--Cohort Structure on the Claim Development in a Run-Off Triangle},
  author={Pittarello, Gabriele and Hiabu, Munir and Villegas, Andr{\'e}s M},
  journal={North American Actuarial Journal},
  pages={1--31},
  year={2025},
  publisher={Taylor \& Francis}
  doi = {10.1080/10920277.2025.2496725},
}
```

## Further Resources

The most recent tutorials and replication files associated with the manuscript can be accessed via [our project website](https://gpitt71.github.io/clmplus/).






