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actuar

actuar is a package providing additional actuarial science functionality to the R statistical system. The project was officially launched in 2005 and is under active development.

Features

The current feature set of the package can be split into five main categories:

  1. Additional probability distributions to model insurance loss amounts and loss frequency (19 continuous heavy tailed distributions, see the list below; the Poisson-inverse Gaussian discrete distribution; zero-truncated and zero-modified extensions of the standard discrete distributions; phase-type distributions);
  2. Loss distributions modeling (extensive support for grouped data; empirical raw and limited moments; minimum distance estimation);
  3. Risk and ruin theory (discretization of the claim amount distribution and computation of the aggregate claim amount distribution; computation of the adjustment coefficient and ruin probabilities);
  4. Simulation of discrete mixtures, compound models and compound hierarchical models;
  5. Credibility theory (Bühlmann, Bühlmann-Straub, hierarchical, regression and linear Bayes models).

The package includes extensive documentation in the form of package vignettes. Each vignette focuses on a feature set of the package. To get the list of available vignettes, enter at the R command prompt:

vignette(package = "actuar")

Installation

You should install the stable version of the package from the Comprehensive R Archive Network (CRAN): using:

install.packages("actuar")

Citation

To cite package actuar in publications see the output of

citation(package = "actuar")

License

actuar is free software licensed under the GNU General Public License (GPL), version 2 or later.

Philosophy

As much as possible, the developers have tried to keep the user interface of the various functions of the package consistent. Moreover, the package follows the general R philosophy of working with model objects. This means that instead of merely returning, say, a vector of probabilities, many functions will return an object containing, among other things, the said probabilities. The object can then be manipulated at one's will using various extraction, summary or plotting functions.

Additional continuous distributions

actuar provides support functions for all the probability distributions found in Appendix A of Loss Models: From Data to Decisions, 4th Edition and not already present in base R, excluding the log-t, but including the loggamma distribution. These distributions mostly fall under the umbrella of extreme value or heavy tailed distributions.

The list of distributions supported by actuar is as follows, using the nomenclature of Loss Models.

Transformed beta family

  • Transformed beta
  • Burr
  • Loglogistic
  • Paralogistic
  • Generalized Pareto
  • Pareto
  • Inverse Burr
  • Inverse Pareto
  • Inverse paralogistic

Transformed gamma family

  • Transformed gamma
  • Inverse transformed gamma
  • Inverse gamma
  • Inverse Weibull
  • Inverse exponential

Other

  • Loggamma
  • Gumbel
  • Inverse Gaussian
  • Single parameter Pareto
  • Generalized beta

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Version

Install

install.packages('actuar')

Monthly Downloads

11,161

Version

2.3-3

License

GPL (>= 2)

Maintainer

Vincent Goulet

Last Published

December 4th, 2019

Functions in actuar (2.3-3)

Gumbel

The Gumbel Distribution
InverseTransformedGamma

The Inverse Transformed Gamma Distribution
Logarithmic

The Logarithmic Distribution
InverseGamma

The Inverse Gamma Distribution
InverseParalogistic

The Inverse Paralogistic Distribution
InverseBurr

The Inverse Burr Distribution
InverseGaussian

The Inverse Gaussian Distribution
InversePareto

The Inverse Pareto Distribution
Loggamma

The Loggamma Distribution
InverseWeibull

The Inverse Weibull Distribution
InverseExponential

The Inverse Exponential Distribution
NormalSupp

Moments and Moment generating function of the Normal Distribution
LognormalMoments

Raw and Limited Moments of the Lognormal Distribution
Loglogistic

The Loglogistic Distribution
TransformedBeta

The Transformed Beta Distribution
Paralogistic

The Paralogistic Distribution
SingleParameterPareto

The Single-parameter Pareto Distribution
Pareto

The Pareto Distribution
TransformedGamma

The Transformed Gamma Distribution
PhaseType

The Phase-type Distribution
PoissonInverseGaussian

The Poisson-Inverse Gaussian Distribution
UniformSupp

Moments and Moment Generating Function of the Uniform Distribution
VaR

Value at Risk
ZeroModifiedLogarithmic

The Zero-Modified Logarithmic Distribution
ZeroModifiedGeometric

The Zero-Modified Geometric Distribution
ZeroModifiedNegativeBinomial

The Zero-Modified Negative Binomial Distribution
WeibullMoments

Raw and Limited Moments of the Weibull Distribution
ZeroTruncatedGeometric

The Zero-Truncated Geometric Distribution
ZeroModifiedBinomial

The Zero-Modified Binomial Distribution
ZeroTruncatedBinomial

The Zero-Truncated Binomial Distribution
ZeroModifiedPoisson

The Zero-Modified Poisson Distribution
discretize

Discretization of a Continuous Distribution
ZeroTruncatedPoisson

The Zero-Truncated Poisson Distribution
adjCoef

Adjustment Coefficient
dental

Individual Dental Claims Data Set
ZeroTruncatedNegativeBinomial

The Zero-Truncated Negative Binomial Distribution
actuar-deprecated

Deprecated Functions in Package actuar
betaint

The “Beta Integral”
cm

Credibility Models
aggregateDist

Aggregate Claim Amount Distribution
coverage

Density and Cumulative Distribution Function for Modified Data
gdental

Grouped Dental Claims Data Set
mde

Minimum Distance Estimation
mean.grouped.data

Arithmetic Mean
ogive

Ogive for Grouped Data
quantile.aggregateDist

Quantiles of Aggregate Claim Amount Distribution
grouped.data

Grouped data
hachemeister

Hachemeister Data Set
hist.grouped.data

Histogram for Grouped Data
emm

Empirical Moments
elev

Empirical Limited Expected Value
rcompound

Simulation from Compound Models
quantile.grouped.data

Quantiles of Grouped Data
simul.summaries

Summary Statistics of a Portfolio
unroll

Display a Two-Dimension Version of a Matrix of Vectors
simul

Simulation from Compound Hierarchical Models
severity

Manipulation of Individual Claim Amounts
ruin

Probability of Ruin
rmixture

Simulation from Discrete Mixtures
GeneralizedBeta

The Generalized Beta Distribution
Extract.grouped.data

Extract or Replace Parts of a Grouped Data Object
ExponentialSupp

Moments and Moment Generating Function of the Exponential Distribution
GammaSupp

Moments and Moment Generating Function of the Gamma Distribution
CTE

Conditional Tail Expectation
ChisqSupp

Moments and Moment Generating Function of the (non-central) Chi-Squared Distribution
GeneralizedPareto

The Generalized Pareto Distribution
BetaMoments

Raw and Limited Moments of the Beta Distribution
Burr

The Burr Distribution