UseRs,
actuar is a package for Actuarial Science. A rather preliminary
version (0.1-3) of the package has been available on CRAN since February 2006.
We now announce the immediate availability of version 0.9-2 sporting a large
number of new features.
Non actuaries behold! There can be some features of interest for you,
especially those related to new probability distribution and to the
manipulation of grouped data.
Since I took the time to write a fairly detailed NEWS file, I'll let it
speak
for itself:
=== actuar: an R package for Actuarial Science ==
Version 0.9-2
============
Major official update. This version is not backward compatible with
the 0.1-x series. Feature of the package can be split in the following
categories: loss distributions modeling, risk theory, credibility
theory.
NEW FEATURES -- LOSS DISTRIBUTIONS
o Functions {d,p,q,r}foo to compute the density function, cumulative
distribution function, quantile function of, and to generate
variates from, all probability distributions of Appendix A of
Klugman et al. (2004), "Loss Models, Second Edition" (except the
inverse gaussian) not already in R. Namely, this adds the following
distributions (the root is what follows the 'd', 'p',
'q' or 'r' in
function names):
Distribution name Root
------------------------- --------------
Burr burr
Generalized beta genbeta
Generalized Pareto genpareto
Inverse Burr invburr
Inverse exponential invexp
Inverse Pareto invpareto
Inverse paralogistic invparalogis
Inverse Weibull invweibull
Loggamma loggamma
Loglogistic llogis
Paralogistic paralogis
Pareto pareto
Single parameter Pareto pareto1
Transformed beta trbeta
Transformed gamma trgamma
All functions are coded in C for efficiency purposes and should
behave exactly like the functions in base R. For all distributions
that have a scale parameter, the corresponding functions have 'rate
= 1' and 'scale = 1/rate' arguments.
o Functions {m,lev}foo to compute the k-th raw (non-central) moment
and k-th limited moment for all the probability distributions
mentioned above, plus the following ones of base R: beta,
exponential, gamma, lognormal and Weibull.
o Facilities to store and manipulate grouped data (stored in an
interval-frequency fashion). Function grouped.data() creates a
grouped data object similar to a data frame. Methods of "[",
"[<-",
mean() and hist() created for objects of class "grouped.data".
o Function ogive() --- with appropriate methods of knots(),
plot(), print() and summary() --- to compute the ogive of grouped
data. Usage is in every respect similar to ecdf().
o Function elev() to compute the empirical limited expected value of a
sample of individual or grouped data.
o Function emm() to compute the k-th empirical raw (non-central)
moment of a sample of individual or grouped data.
o Function mde() to compute minimum distance estimators from a sample
of individual or grouped data using one of three distance measures:
Cramer-von Mises (CvM), chi-square, layer average severity
(LAS). Usage is similar to fitdistr() of package 'MASS'.
o Function coverage() to obtain the pdf or cdf of the payment per
payment or payment per loss random variable under any combination of
the following coverage modifications: ordinary of franchise
deductible, policy limit, coinsurance, inflation. The result is a
function that can be used in fitting models to data subject to such
coverage modifications.
o Individual dental claims data set 'dental' and grouped dental claims
data set 'gdental' of Klugman et al. (2004), "Loss Models, Second
Edition".
NEW FEATURES -- RISK THEORY
o Function aggregateDist() returns a function to compute the
cumulative distribution function of the total amount of claims
random variable for an insurance portfolio using any of the
following five methods:
1. exact calculation by convolutions (using function convolve() of
package 'stats';
2. recursive calculation using Panjer's algorithm;
3. normal approximation;
4. normal power approximation;
5. simulation.
The modular conception of aggregateDist() allows for easy inclusion
of additional methods. There are special methods of print(),
summary(), quantile() and mean() for objects of class
"aggregateDist". The objects otherwise inherit from classes
"ecdf"
(for methods 1, 2 and 3) and "function".
See also the "Deprecated, defunct or no backward compatibility"
section below.
o Function discretize() to discretize a continuous distribution using
any of the following four methods:
1. upper discretization, where the discretized cdf is always above
the true cdf;
2. lower discretization, where the discretized cdf is always under
the true cdf;
3. rounding, where the true cdf passes through the midpoints of the
intervals of the discretized cdf;
4. first moment matching of the discretized and true distributions.
Usage is similar to curve() of package 'graphics'. Again, the
modular conception allows for easy inclusion of additional
discretization methods.
NEW FEATURES -- CREDIBILITY THEORY
o Function simpf() can now simulate data for hierarchical portfolios
of any number of levels. Model specification changed completely; see
the "Deprecated, defunct or no backward compatibility" below. The
function is also significantly (~10x) faster than the previous
version.
o Generic function severity() defined mostly to provide a method for
objects of class "simpf"; see below.
o Methods of aggregate(), frequency(), severity() and weights() to
extract information from objects of class "simpf":
1. aggregate() returns the matrix of aggregate claim amounts per
node;
2. frequency() returns the matrix of the number of claims per node;
3. severity() returns the matrix of individual claim amounts per
node;
4. weights() returns the matrix of weights corresponding to the
data.
Summaries can be done in various ways; see ?simpf.summaries
o Function cm() (for "_c_redibility _m_odel") to compute structure
parameters estimators for hierarchical credibility models, including
the B?hlmann and B?hlmann-Straub models. Usage is similar to lm() of
packages 'stats' in that the hierarchical structure is specified by
means of a formula object and data is extracted from a matrix or
data frame. There are special methods of print(), summary() for
objects of class "cm". Credibility premiums are computed using a
method of predict(); see below.
For simple B?hlmann and B?hlmann-Straub models, bstraub() remains
simpler to use and faster.
o Function bstraub() now returns an object of class "bstraub" for
which there exist print and summary methods. The function no longer
computes the credibility premiums; see the "Deprecated,
defunct or no backward compatibility" below.
o Methods of predict() for objects of class "cm" and
"bstraub" created
to actually compute the credibility premiums of credibility
models. Function predict.cm() can return the premiums for specific
levels of a hierarchical portfolio only.
OTHER NEW FEATURES
o Function unroll() to unlist a list with a "dim" attribute of length
0, 1 or 2 (that is, a vector or matrix of vectors) according
to a specific dimension. Currently identical to severity.default()
by lack of a better usage of the default method of severity().
o Three new demos corresponding to the three main fields of actuarial
science covered by the package.
o French translations of the error and warning messages.
o The package now has a name space.
DEPRECATED, DEFUNCT OR NO BACKWARD COMPATIBILITY
o Function panjer(), although still present in the package, should no
longer be used directly. Recursive calculation of the aggregate
claim amount should be done with aggregateDist(). Further, the
function is not backward compatible: model specification has
changed, discretization of the claim amount distribution should now
be done with discretize(), and the function now returns a function
to compute the cdf instead of a simple vector of probabilities.
o Model specification for simpf() changed completely and is not
backward compatible with previous versions of the package. The new
scheme allows for much more general models.
o Function rearrangepf() is defunct and has been replaced by methods
of aggregate(), frequency() and severity().
o Function bstraub() no longer computes the credibility premiums. One
should now instead use predict() for this.
o The data set 'hachemeister' is no longer a list but rather a matrix
with a state specification.
Version 0.1-3
============
Fixed the dependency on R >= 2.1.0 since the package uses function
isTRUE().
Version 0.1-2
============
- First public release.
- Fixed an important bug in bstraub(): when calculating the range of
the weights matrix, NAs were not excluded.
- Miscellaneous documentation corrections.
Version 0.1-1
============
- Initial release.
- Contains functions bstraub(), simpf(), rearrangepf() and panjer(),
and the dataset hachemeister.
[There has been a very short lived version 0.9-1 on CRAN. Please ignore this
version altogether.]
Collaboration is welcome. Please contact me directly.
--
Vincent Goulet, Associate Professor
?cole d'actuariat
Universit? Laval, Qu?bec
Vincent.Goulet at act.ulaval.ca http://vgoulet.act.ulaval.ca
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