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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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simulationmachine

Lifecycle: experimentalTravis build status

This package implements the claims history simulation algorithm detailed in An Individual Claims History Simulation Machine 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 with:

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

Example

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

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#> A simulation charm for `simulation_machine`#> #> Each record is:#> - A snapshot of a claim's incremental paid loss and claim status#> at a development year.#> #> Specs:#> - Expected number of claims: 50,000#> - LOB distribution: 0.25, 0.25, 0.3, 0.2#> - Inflation: 0.01, 0.01, 0.01, 0.01#> - SD of claim sizes: 0.85,#> - SD of recovery sizes: 0.85

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

library(dplyr)
records<- conjure(charm, seed=100)
glimpse(records)
#> Observations: 603,324#> Variables: 11#> $ claim_id <chr> "1", "1", "1", "1", "1", "1", "1", "1", "1", "…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1994…#> $ development_year <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 0, 1, 2,…#> $ accident_quarter <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 4, 4, 4…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ lob <chr> "3", "3", "3", "3", "3", "3", "3", "3", "3", "…#> $ cc <chr> "42", "42", "42", "42", "42", "42", "42", "42"…#> $ age <int> 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65…#> $ injured_part <chr> "51", "51", "51", "51", "51", "51", "51", "51"…#> $ paid_loss <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4913, 0, 0…#> $ claim_status_open <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…

Let’s see how many claims we drew:

records %>% distinct(claim_id) %>% count()
#> # A tibble: 1 x 1#> n#> <int>#> 1 50277

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

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)
#> Observations: 50,277#> Variables: 32#> $ claim_id <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9"…#> $ accident_year <int> 1994, 1994, 1994, 1994, 1994, 1994, 1994, 1…#> $ accident_quarter <dbl> 1, 4, 4, 2, 1, 1, 1, 3, 4, 4, 2, 2, 4, 3, 1…#> $ report_delay <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ lob <chr> "3", "3", "3", "4", "2", "1", "3", "1", "3"…#> $ cc <chr> "42", "39", "26", "8", "50", "22", "43", "1…#> $ age <int> 65, 52, 23, 54, 24, 53, 39, 40, 27, 43, 55,…#> $ injured_part <chr> "51", "53", "70", "36", "36", "53", "51", "…#> $ paid_loss_0 <dbl> 0, 4913, 0, 458, 1158, 376, 0, 285, 0, 0, 0…#> $ paid_loss_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3389, 0, 0, 0, 0…#> $ paid_loss_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_6 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_7 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_8 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_9 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_10 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ paid_loss_11 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_0 <int> 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0…#> $ claim_status_open_1 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…#> $ claim_status_open_2 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_3 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_4 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_5 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_6 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_7 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_8 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_9 <int> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_10 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…#> $ claim_status_open_11 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

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