Package {NetSimR}

NetSimR: A non-life insurance package for computing various statistics

Description

The NetSimR package provides five categories of functions:

1. Capped means, Exposure and ILF curve from various severity distributions

2. Pure IBNR and UPR earned periods

3. Sliced distributions

4. Frequency-severity claims simulation

5. Shiny apps for simulating claims and fitting distributions

NetSimR mean functions

SlicedGammaParetoMean SlicedLNormParetoMean

NetSimR capped mean functions

GammaCappedMean LNormCappedMean ParetoCappedMean SlicedGammaParetoCappedMean SlicedLNormParetoCappedMean

NetSimR exposure curve functions

ExposureCurveGamma ExposureCurveLNorm ExposureCurvePareto ExposureCurveSlicedGammaPareto ExposureCurveSlicedLNormPareto

NetSimR ILF curve functions

ILFGamma ILFLNorm ILFPareto ILFSlicedGammaPareto ILFSlicedLNormPareto

NetSimR pure IBNR functions

PureIBNRGamma PureIBNRLNorm

NetSimR Sliced distribution functions

dSlicedGammaPareto dSlicedLNormPareto pSlicedGammaPareto pSlicedLNormPareto qSlicedGammaPareto qSlicedLNormPareto

NetSimR claims simulation functions

simulate_claims simulate_function

NetSimR Shiny apps

run_shiny_simulator run_shiny_distribution_fitting_tool run_shiny_glm_fitting_tool

Author(s)

Maintainer: Yiannis Parizas yiannis.parizas@gmail.com

Authors:

• Yiannis Parizas yiannis.parizas@gmail.com

Exposure Curve from a Gamma severity distribution

Description

Exposure Curve from a Gamma severity distribution

Usage

ExposureCurveGamma(x, shape, rate)

Arguments

Value

The value of the Exposure curve at x with Claim Severity from a Gamma distribution with parameters shape and rate.

Examples

ExposureCurveGamma(700,1,0.0005)
ExposureCurveGamma(1000,1.5,0.0006)

Exposure Curve from LogNormal a severity distribution

Description

Exposure Curve from LogNormal a severity distribution

Usage

ExposureCurveLNorm(x, mu, sigma)

Arguments

Value

The value of the Exposure curve at x with Claim Severity from a LogNormal distribution with parameters mu and sigma.

Examples

ExposureCurveLNorm(2000,6,1.5)
ExposureCurveLNorm(1000,5,1.6)

Exposure Curve from a Pareto severity distribution

Description

Exposure Curve from a Pareto severity distribution

Usage

ExposureCurvePareto(x, scale, shape)

Arguments

Value

The value of the Exposure curve at x with Claim Severity from a Pareto distribution with parameters scale and shape. The exposure curve divides by the mean, which is infinite when shape <= 1; the function returns 0 in that case.

Examples

ExposureCurvePareto(700,500,1.2)
ExposureCurvePareto(20000,200,1.1)

Exposure Curve from a Sliced Gamma Pareto severity distribution

Description

Exposure Curve from a Sliced Gamma Pareto severity distribution

Usage

ExposureCurveSlicedGammaPareto(x, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The value of the Exposure curve at x with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape. The exposure curve divides by the mean, which is infinite when PShape <= 1; the function returns 0 in that case.

Examples

ExposureCurveSlicedGammaPareto(3000,1,0.0005,1000,1.2)
ExposureCurveSlicedGammaPareto(1000,1.1,0.0006,2000,1.6)
ExposureCurveSlicedGammaPareto(2000,1.2,0.0004,3000,1.4)

Exposure Curve from a Sliced LogNormal Pareto severity distribution

Description

Exposure Curve from a Sliced LogNormal Pareto severity distribution

Usage

ExposureCurveSlicedLNormPareto(x, mu, sigma, SlicePoint, shape)

Arguments

Value

The value of the Exposure curve at x with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape. The exposure curve divides by the mean, which is infinite when shape <= 1; the function returns 0 in that case.

Examples

ExposureCurveSlicedLNormPareto(1200,6,1.5,1000,1.2)
ExposureCurveSlicedLNormPareto(4000,7,1.6,3000,1.4)

Server function for the GLM Fitting tool application

Description

Server function for the GLM Fitting tool application

Usage

GLMFittingToolServer(input, output, session)

Arguments

Value

Called by shiny for its side effects, the outputs and observers of a session; the value is not used.

User interface of the Shiny GLM fitting tool

Description

A function of the request, so that it is built when the app starts, after every helper of the package is defined.

Usage

GLMFittingToolUI(request)

Arguments

Value

The user interface of the application, a bslib navbar page.

Gamma capped mean

Description

Gamma capped mean

Usage

GammaCappedMean(cap, shape, rate)

Arguments

Value

The mean of the claim severity capped at cap with a Gamma distribution with parameters shape and rate.

Examples

GammaCappedMean(700,1,0.0005)
GammaCappedMean(1000,1.5,0.0006)

Upper incomplete gamma function

Description

Upper incomplete gamma function

Usage

IGamma(a, x)

Arguments

Value

The value of the upper incomplete gamma function at x with shape parameter a, i.e. gamma(a) * pgamma(x, a, lower.tail = FALSE).

Examples

IGamma(1,1)
IGamma(0.1,2)

Increased Limit Factor Curve from a Gamma severity distribution

Description

Increased Limit Factor Curve from a Gamma severity distribution

Usage

ILFGamma(xLow, xHigh, shape, rate)

Arguments

Value

The value of the Increased Limit Factor curve from xLow to xHigh with Claim Severity from a Gamma distribution with parameters shape and rate.

Examples

ILFGamma(700,1000,1,0.0005)
ILFGamma(1000,1200,1.5,0.0006)

Increased Limit Factor Curve from a LogNormal severity distribution

Description

Increased Limit Factor Curve from a LogNormal severity distribution

Usage

ILFLNorm(xLow, xHigh, mu, sigma)

Arguments

Value

The value of the Increased Limit Factor curve from xLow to xHigh with Claim Severity from a LogNormal distribution with parameters mu and sigma.

Examples

ILFLNorm(1000,2000,6,1.5)
ILFLNorm(1000,1500,5,1.6)

Increased Limit Factor Curve from a Pareto severity distribution

Description

Increased Limit Factor Curve from a Pareto severity distribution

Usage

ILFPareto(xLow, xHigh, scale, shape)

Arguments

Value

The value of the Increased Limit Factor curve from xLow to xHigh with Claim Severity from a Pareto distribution with parameters scale and shape.

Examples

ILFPareto(700,1200,500,1.2)
ILFPareto(1200,20000,200,1.1)

Increased Limit Factor Curve from a Sliced Gamma Pareto severity distribution

Description

Increased Limit Factor Curve from a Sliced Gamma Pareto severity distribution

Usage

ILFSlicedGammaPareto(xLow, xHigh, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The value of the Increased Limit Factor curve from xLow to xHigh with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

ILFSlicedGammaPareto(2000,3000,1,0.0005,1000,1.2)
ILFSlicedGammaPareto(800,1000,1.1,0.0006,2000,1.6)
ILFSlicedGammaPareto(1200,2000,1.2,0.0004,3000,1.4)

Increased Limit Factor Curve from a Sliced LogNormal Pareto severity distribution

Description

Increased Limit Factor Curve from a Sliced LogNormal Pareto severity distribution

Usage

ILFSlicedLNormPareto(xLow, xHigh, mu, sigma, SlicePoint, shape)

Arguments

Value

The value of the Increased Limit Factor curve from xLow to xHigh with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

ILFSlicedLNormPareto(800,1200,6,1.5,1000,1.2)
ILFSlicedLNormPareto(2000,4000,7,1.6,3000,1.4)

Lognormal capped mean

Description

Lognormal capped mean

Usage

LNormCappedMean(cap, mu, sigma)

Arguments

Value

The mean of the claim severity capped at cap with a LogNormal distribution with parameters mu and sigma.

Examples

LNormCappedMean(2000,6,1.5)
LNormCappedMean(1000,5,1.6)

Pareto capped mean

Description

Pareto capped mean

Usage

ParetoCappedMean(cap, scale, shape)

Arguments

Value

The mean of the claim severity capped at cap with a Pareto distribution with parameters scale and shape. A cap at or below scale is returned unchanged, as no claim is smaller than scale. The arguments are recycled to a common length.

Examples

ParetoCappedMean(600,200,1.2)
ParetoCappedMean(800,100,1)
ParetoCappedMean(1000,500,0.8)
ParetoCappedMean(50,100,2)

Pareto capped mean intermediary calculation

Description

Pareto capped mean intermediary calculation

Usage

ParetoCappedMeanCalc(cap, scale, shape)

Arguments

Value

An interim calculation for the mean of the claim severity capped at cap with a Pareto distribution with parameters scale and shape. It is the closed form for cap >= scale and shape != 1; use ParetoCappedMean for the capped mean itself.

Examples

ParetoCappedMeanCalc(800,100,1.1)
ParetoCappedMeanCalc(1000,500,0.9)

Pure IBNR exposure from a Gamma reporting delay distribution

Description

Pure IBNR exposure from a Gamma reporting delay distribution

Usage

PureIBNRGamma(IncDate, ExpDate, ValDate, shape, rate)

Arguments

Value

Unearned and Pure IBNR exposure in days and as a percentage of the period's duration, where the reporting delay has a Gamma distribution with parameters shape and rate.

Examples

Dates = data.frame(
    inceptionDate = c("01/01/2006", "01/07/2006", "01/01/2007")
    ,expiryDate = c("31/12/2006", "30/06/2007", "31/12/2007")
)

Dates$inceptionDate<-as.POSIXct(Dates$inceptionDate, format="%d/%m/%Y")

Dates$expiryDate<-as.POSIXct(Dates$expiryDate, format="%d/%m/%Y")

ValuationDate<-as.POSIXct("30/10/2007", format="%d/%m/%Y")

PureIBNRGamma(Dates$inceptionDate,Dates$expiryDate,ValuationDate,7,0.15)

Pure IBNR exposure from a LogNormal reporting delay distribution

Description

Pure IBNR exposure from a LogNormal reporting delay distribution

Usage

PureIBNRLNorm(IncDate, ExpDate, ValDate, mu, sigma)

Arguments

Value

Unearned and Pure IBNR exposure in days and as a percentage of the period's duration, where the reporting delay has a LogNormal distribution with parameters mu and sigma.

Examples

Dates = data.frame(
    inceptionDate = c("01/01/2006", "01/07/2006", "01/01/2007")
    ,expiryDate = c("31/12/2006", "30/06/2007", "31/12/2007")
)

Dates$inceptionDate<-as.POSIXct(Dates$inceptionDate, format="%d/%m/%Y")

Dates$expiryDate<-as.POSIXct(Dates$expiryDate, format="%d/%m/%Y")

ValuationDate<-as.POSIXct("30/10/2007", format="%d/%m/%Y")

PureIBNRLNorm(Dates$inceptionDate,Dates$expiryDate,ValuationDate,4,1.5)

Sliced Gamma Pareto capped mean

Description

Sliced Gamma Pareto capped mean

Usage

SlicedGammaParetoCappedMean(cap, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The mean of the claim severity capped at cap with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

SlicedGammaParetoCappedMean(3000,1,0.0005,1000,1.2)
SlicedGammaParetoCappedMean(1000,1.1,0.0006,2000,1.6)
SlicedGammaParetoCappedMean(2000,1.2,0.0004,3000,1.4)

Sliced Gamma Pareto mean

Description

Sliced Gamma Pareto mean

Usage

SlicedGammaParetoMean(GShape, GRate, SlicePoint, PShape)

Arguments

Value

The mean of the claim severity with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

SlicedGammaParetoMean(1,0.0005,1000,1.2)
SlicedGammaParetoMean(1.1,0.0006,2000,1.6)
SlicedGammaParetoMean(1.2,0.0004,3000,1.4)

Sliced LogNormal Pareto capped mean

Description

Sliced LogNormal Pareto capped mean

Usage

SlicedLNormParetoCappedMean(cap, mu, sigma, SlicePoint, shape)

Arguments

Value

The mean of the claim severity capped at cap with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

SlicedLNormParetoCappedMean(1200,6,1.5,1000,1.2)
SlicedLNormParetoCappedMean(2500,6.5,1.4,2000,1.6)
SlicedLNormParetoCappedMean(4000,7,1.6,3000,1.4)

Sliced LogNormal Pareto mean

Description

Sliced LogNormal Pareto mean

Usage

SlicedLNormParetoMean(mu, sigma, SlicePoint, shape)

Arguments

Value

The mean of the claim severity with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

SlicedLNormParetoMean(6,1.5,1000,1.2)
SlicedLNormParetoMean(6.5,1.4,2000,1.6)
SlicedLNormParetoMean(7,1.6,3000,1.4)

Apply a deductible and limit to claims

Description

Apply a deductible and limit to claims

Usage

apply_deductible_limit(
  gross_claims_data,
  reinsurance_structure,
  deductible,
  limit
)

Arguments

Value

The ceded claims for the structure, with the chosen deductible and limit.

Examples

apply_deductible_limit(c(100, 50, 20), 'Limited Layer', 40, 20)
apply_deductible_limit(c(100, 50, 20), 'Limited Layer', 10, 30)

Apply severity cap function

Description

Apply severity cap function

Usage

apply_severity_cap(claims, severity_cap_boolean, severity_cap_amount)

Arguments

Value

If severity_cap_boolean is true, then will return the minimum of severity_cap_amount or claims otherwise will return claims. The operation is vectorised.

The probability density function (pdf) of a Sliced Gamma Pareto severity distribution

Description

The probability density function (pdf) of a Sliced Gamma Pareto severity distribution

Usage

dSlicedGammaPareto(x, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The value of the probability density function (pdf) at x with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

dSlicedGammaPareto(3000,1,0.0005,1000,1.2)
dSlicedGammaPareto(1000,1.1,0.0006,2000,1.6)
dSlicedGammaPareto(2000,1.2,0.0004,3000,1.4)

The probability density function (pdf) of a Sliced LogNormal Pareto severity distribution

Description

The probability density function (pdf) of a Sliced LogNormal Pareto severity distribution

Usage

dSlicedLNormPareto(x, mu, sigma, SlicePoint, shape)

Arguments

Value

The value of the probability density function (pdf) at x with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

dSlicedLNormPareto(1200,6,1.5,1000,1.2)
dSlicedLNormPareto(4000,7,1.6,3000,1.4)

The class of the distribution objects

Description

Each object describes one frequency or severity distribution of the simulator: its parameters (ids used as app input ids, labels, allowed ranges and whether they must be whole numbers) and the functions the simulator needs. simulate_func draws values; moments_func gives the mean and standard deviation. Severity distributions also have survival_func and tail_quantile_func (the upper-tail probability and its inverse), used to splice Pareto tails and to draw only the claims that reach a layer, and optionally sum_func, which draws the total of a given number of claims in one step. Frequency distributions may have split_func, which draws the number of claims and how many of them are large. Allowed ranges are param_min_values and param_max_values (NA for no bound), with param_min_strict and param_max_strict marking bounds the value may not equal.

Server function for the Distribution Fitting tool application

Description

Server function for the Distribution Fitting tool application

Usage

distribution_fitting_tool_Server(input, output, session)

Arguments

Value

Called by shiny for its side effects, the outputs and observers of a session; the value is not used.

User interface of the Shiny distribution fitting tool

Description

The page is built when the package is installed; run_shiny_distribution_fitting_tool() pairs it with distribution_fitting_tool_Server.

Usage

distribution_fitting_tool_UI

Value

The user interface of the application, a bslib navbar page.

Error function

Description

Error function

Usage

erf(x)

Arguments

Value

The value of the error function at x.

Examples

erf(0.1)
erf(0.5)

A vector with the frequency distribution objects

Description

A vector with the frequency distribution objects

Usage

freq_dist_options

Value

The frequency distribution objects.

A data frame with the frequency distribution parameter placeholders

Description

A data frame with the frequency distribution parameter placeholders

Usage

freq_dist_parameter_placeholders

Value

The frequency distribution parameter placeholders.

Parameter to set the maximum number of pareto slices

Description

Parameter to set the maximum number of pareto slices

Usage

max_number_of_pareto_slices

Value

The maximum number of Pareto Slices.

The cumulative density function (cdf) of a Sliced Gamma-Pareto severity distribution

Description

The cumulative density function (cdf) of a Sliced Gamma-Pareto severity distribution

Usage

pSlicedGammaPareto(x, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The value of the cumulative density function (cdf) at x with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

pSlicedGammaPareto(3000,1,0.0005,1000,1.2)
pSlicedGammaPareto(1000,1.1,0.0006,2000,1.6)
pSlicedGammaPareto(2000,1.2,0.0004,3000,1.4)

The cumulative density function (cdf) of a Sliced LogNormal Pareto severity distribution

Description

The cumulative density function (cdf) of a Sliced LogNormal Pareto severity distribution

Usage

pSlicedLNormPareto(x, mu, sigma, SlicePoint, shape)

Arguments

Value

The value of the cumulative density function (cdf) at x with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

pSlicedLNormPareto(1200,6,1.5,1000,1.2)
pSlicedLNormPareto(4000,7,1.6,3000,1.4)

The inverse cumulative density function of a Sliced Gamma Pareto severity distribution

Description

The inverse cumulative density function of a Sliced Gamma Pareto severity distribution

Usage

qSlicedGammaPareto(q, GShape, GRate, SlicePoint, PShape)

Arguments

Value

The value of the inverse cumulative density function at q with an attritional claim Gamma distribution with parameters GShape and GRate and a large claim Pareto distribution with parameters SlicePoint and PShape.

Examples

qSlicedGammaPareto(0.5,1,0.0005,1000,1.2)
qSlicedGammaPareto(0.2,1.1,0.0006,2000,1.6)
qSlicedGammaPareto(0.8,1.2,0.0004,3000,1.4)

The inverse cumulative density function of a Sliced LogNormal Pareto severity distribution

Description

The inverse cumulative density function of a Sliced LogNormal Pareto severity distribution

Usage

qSlicedLNormPareto(q, mu, sigma, SlicePoint, shape)

Arguments

Value

The value of the inverse cumulative density function at q with an attritional claim LogNormal distribution with parameters mu and sigma and a large claim Pareto distribution with parameters SlicePoint and shape.

Examples

qSlicedLNormPareto(0.5,6,1.5,1000,1.2)
qSlicedLNormPareto(0.7,7,1.6,3000,1.4)

A vector with the reinsurance structure options

Description

A vector with the reinsurance structure options

Usage

reinsurance_structures_options

Value

The reinsurance structure options

A function to run the distribution fitting tool application

Description

A function to run the distribution fitting tool application

Usage

run_shiny_distribution_fitting_tool()

Value

A shiny app object. Printing it, as happens when the function is called at the console, opens the application; it can also be passed to shiny::runApp().

Examples

if (interactive()) {
  run_shiny_distribution_fitting_tool()
}

A function to run the GLM fitting tool application

Description

A function to run the GLM fitting tool application

Usage

run_shiny_glm_fitting_tool()

Value

A shiny app object. Printing it, as happens when the function is called at the console, opens the application; it can also be passed to shiny::runApp().

Examples

if (interactive()) {
  run_shiny_glm_fitting_tool()
}

A function to run the shiny simulator application

Description

A function to run the shiny simulator application

Usage

run_shiny_simulator()

Value

Opens the shiny simulator application

Examples

if (interactive()) {
  run_shiny_simulator()
}

A vector with the severity distribution objects

Description

A named list of distributionClass objects, one for each severity distribution of the simulator: Normal, LogNormal, Gamma, Exponential, Pareto and Fixed_Severity. The names are the values accepted by the sevDistr argument of simulate_function(). The Normal distribution has its own parameter ids (normal_mean, normal_sd), so that switching between the Normal and the Log-Normal in the app does not carry values across.

Usage

sev_dist_options

Value

The severity distribution objects.

A data frame with the severity distribution parameter placeholders

Description

A data frame with the severity distribution parameter placeholders

Usage

sev_dist_parameter_placeholders

Value

The severity distribution parameter placeholders.

Server function for the Shiny Simulator application

Description

Server function for the Shiny Simulator application

Usage

shiny_simulator_server(input, output, session)

Arguments

Value

Returns server rendering for the shiny application.

UI file for the Shiny NetSimR Simulator Tool

Description

UI file for the Shiny NetSimR Simulator Tool

Usage

shiny_simulator_ui

Value

Returns the UI code for the shiny application.

Simulate claims with a frequency-severity model

Description

A simpler interface to simulate_function, with short argument names and defaults for everything optional. Leaving an option out (NULL) switches the feature off: no seed, no Pareto slices, no cap, no reinstatement limit.

Usage

simulate_claims(
  n_sims,
  frequency,
  frequency_params,
  severity,
  severity_params,
  seed = NULL,
  truncate_at_zero = FALSE,
  pareto_thresholds = NULL,
  pareto_alphas = NULL,
  severity_cap = NULL,
  eel_layer = "none",
  eel_deductible = NULL,
  eel_limit = NULL,
  eel_reinstatements = NULL,
  agg_layer = "none",
  agg_deductible = NULL,
  agg_limit = NULL,
  parallel = FALSE,
  chunk_size = NULL,
  gross = TRUE,
  shortcuts = TRUE,
  progress = NULL
)

Arguments

Details

Distributions are chosen by name; case, spaces and underscores are ignored, so "Negative Binomial", "negative_binomial" and "Negative_Binomial" are the same. Their parameters are given in the order below, or named with these names:

Layers are "none", "unlimited" (everything above the deductible), "limited" (the limit excess of the deductible) or "exclude" (the claims with that layer removed). The each-and-every-loss layer applies to every claim; the aggregate layer applies to each simulation's total after the each-and-every-loss layer.

Value

A data frame with one row per simulation: claim_counts, total_claims (after the layers), gross_claims (before them, unless gross = FALSE) and, with limited reinstatements, number_of_reinstatements_used.

See Also

simulate_function, which this calls, and run_shiny_simulator for the same model in an app.

Examples

# 10,000 years of Poisson claim counts with Log-Normal claim sizes
claims <- simulate_claims(
  10000, frequency = "Poisson", frequency_params = 3,
  severity = "LogNormal", severity_params = c(meanlog = 8, sdlog = 1.5), seed = 1
)
summary(claims$total_claims)

# a Pareto tail above 100,000, and a layer of 50,000 excess of 20,000 on each
# claim with two reinstatements
ceded <- simulate_claims(
  10000, "Poisson", 3, "LogNormal", c(8, 1.5), seed = 1,
  pareto_thresholds = 100000, pareto_alphas = 1.5,
  eel_layer = "limited", eel_deductible = 20000, eel_limit = 50000,
  eel_reinstatements = 2
)
mean(ceded$total_claims)

Simulate insurance claims with reinsurance structures

Description

A function to simulate frequency - severity of insurance claims using chunked vectorisation. The function applies severity cap, reinsurance structure for each and every loss claim, reinsurance structure for aggregate claims, and allows for piecewise pareto slices

Usage

simulate_function(
  numOfSimulations,
  freq_params,
  sev_params,
  seedSetBinary = FALSE,
  seedValue = NULL,
  freqDistr,
  sevDistr,
  paretoSlice = FALSE,
  pareto_slice_times = NULL,
  slice_pareto_alphas = NULL,
  slice_pareto_x_ms = NULL,
  sevCapBinary = FALSE,
  sev_cap_amount = NULL,
  reinsuranceStructureEEL = "No Reinsurance Structure",
  reinsurance_structure_eel_dedctible_amount = NULL,
  reinsurance_structure_eel_limit_amount = NULL,
  reinsuranceStructureAL = "No Reinsurance Structure",
  reinsurance_structure_al_dedctible_amount = NULL,
  reinsurance_structure_al_limit_amount = NULL,
  reinsuranceStructureLimitedReinstatements = FALSE,
  reinsuranceStructureReinstatementLimit = NULL,
  multiprocessing = FALSE,
  sevTruncateAtZero = FALSE,
  chunk_size = NULL,
  gross = TRUE,
  shortcuts = TRUE,
  progress = NULL
)

Arguments

Details

Random numbers: each chunk of simulations uses its own L'Ecuyer-CMRG random stream, derived from one seed, so a run gives the same results whether or not it runs in parallel. With seedSetBinary = TRUE the run is reproducible from seedValue and the caller's random number stream is left unchanged; otherwise the seed is drawn from the caller's stream, so set.seed() before the call also makes it reproducible. The streams always use Inversion for normal draws and Rejection sampling, so a seed gives the same results whatever the caller's RNGkind(), which is restored afterwards. Results depend on the chunk size, which by default adapts to the expected number of claims per simulation.

Value

A data frame with one row per simulation: the claim count, the total claims after the reinsurance structures, the gross total claims before them (unless gross = FALSE), and the number of reinstatements used (when reinstatements are limited). Stops with an error that names any required setting that is missing or invalid.

Examples

# 1,000 simulated years of Poisson claim counts with Normal claim sizes, no reinsurance
results <- simulate_function(
  numOfSimulations = 1000, freq_params = 3, sev_params = c(1000, 200),
  seedSetBinary = TRUE, seedValue = 1, freqDistr = "Poisson", sevDistr = "Normal"
)
summary(results$total_claims)

# the same claims ceded to a layer of 1,500 excess of 800 on each claim
layer <- simulate_function(
  numOfSimulations = 1000, freq_params = 3, sev_params = c(1000, 200),
  seedSetBinary = TRUE, seedValue = 1, freqDistr = "Poisson", sevDistr = "Normal",
  reinsuranceStructureEEL = "Limited Layer",
  reinsurance_structure_eel_dedctible_amount = 800,
  reinsurance_structure_eel_limit_amount = 1500
)
mean(layer$total_claims)