README.Rmd

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output: github_document
---

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# simulationmachine

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This package implements the claims history simulation algorithm detailed in [*An Individual Claims History Simulation Machine*](https://www.mdpi.com/2227-9091/6/2/29) by Andrea Gabrielli and Mario V. Wüthrich. The goal is to provide an easy-to-use interface for generating claims data that can be used for loss reserving research.

## Installation

You can install the development version from [GitHub](https://github.com/) with:

``` r
# install.packages("remotes")
remotes::install_github("kasaai/simulationmachine")
```
## Example

First, we can specify the parameters of a simulation using `simulation_machine()`:

```{r}
library(simulationmachine)

charm <- simulation_machine(
  num_claims = 50000, 
  lob_distribution = c(0.25, 0.25, 0.30, 0.20), 
  inflation = c(0.01, 0.01, 0.01, 0.01), 
  sd_claim = 0.85, 
  sd_recovery = 0.85
)

charm
```

Once we have the charm object, we can use `conjure()` to perform the simulation.

```{r, message=FALSE}
library(dplyr)
records <- conjure(charm, seed = 100)
glimpse(records)
```

Let's see how many claims we drew:

```{r}
records %>% 
  distinct(claim_id) %>% 
  count()
```

If you prefer to have each row of the dataset to correspond to a claim, you can simply pivot the data with tidyr:

```{r}
records_wide <- records %>% 
  tidyr::pivot_wider(
    names_from = development_year, 
    values_from = c(paid_loss, claim_status_open),
    values_fill = list(paid_loss = 0)
  )

glimpse(records_wide)
````

-----

Please note that this project is released with a [Contributor Code of Conduct](https://github.com/kasaai/community/blob/master/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.