[R-pkgs] New version of actuar

[Vincent Goulet]

UseRs,

Version 0.9-4 of actuar should be making its way to CRAN mirrors. The  
main highlights of this new version are speed enhancements for a few  
functions, support for phase-type distributions and functions for ruin  
theory.

The relevant section of the NEWS file follows

Version 0.9-4
=============

Maintenance and new features release.

NEW FEATURES -- LOSS DISTRIBUTIONS

   o Functions mgffoo() to compute the moment (or cumulant if 'log =
     TRUE') generating function of the following distributions:
     chi-square, exponential, gamma, inverse gaussian (from package
     SuppDists), inverse gamma, normal, uniform and phase-type (see
     below).

   o Functions mfoo() to compute the raw moments of all the probability
     distributions supported in the package and the following of base
     R: chi-square, exponential, gamma, inverse gaussian (from package
     SuppDists), inverse gamma, normal, uniform.

   o Functions {d,p,mgf,m,r}phtype() to compute the probability density
     function, cumulative distribution function, moment generating
     function, raw moments of, and to generate variates from,
     phase-type distributions.

NEW FEATURES -- RISK THEORY

   o Function VaR() with a method for objects of class "aggregateDist"
     to compute the Value at Risk of a distribution.

   o Function CTE() with a method for objects of class "aggregateDist"
     to compute the Conditional Tail Expectation of a distribution.

   o Function adjCoef() to compute the adjustment coefficient in ruin
     theory. If proportional or excess-of-loss reinsurance is included
     in the model, adjCoef() returns a function to compute the
     adjustment coefficient for given limits. A plot method is also
     included.

   o Function ruin() returns a function to compute the infinite time
     probability of ruin for given initial surpluses in the
     Cramér-Lundberg and Sparre Andersen models. Most calculations are
     done using the cdf of phase-type distributions as per Asmussen and
     Rolski (1991).

   o Calculations of the aggregate claim distribution using the
     recursive method much faster now that recursions are done in C.

NEW FEATURES -- CREDIBILITY THEORY

   o Modular rewrite of cm(): the function now calls internal functions
     to carry calculations for each supported credibility model. This
     is more efficient.

   o Basic support for the regression model of Hachemeister in function
     cm().

   o For the hierarchical credibility model: support for the variance
     components estimators of Bühlmann and Gisler (2005) and Ohlsson
     (2005). Support remains for iterative pseudo-estimators.

   o Calculations of iterative pseudo-estimators in hierarchical
     credibility are much faster now that they are done in C.

OTHER NEW FEATURES

   o Four new vignettes: introduction to the package and presentation
     of the features in loss distributions, risk theory and credibility
     theory.

   o Portfolio simulation material of the "credibility" demo moved to
     demo "simulation".

USER-VISIBLE CHANGES

   o Argument 'approx.lin' of quantile.aggregateDist() renamed
     'smooth'.

   o Function aggregateDist() gains a 'maxit' argument for the maximum
     number of recursions when using Panjer's algorithm. This is to
     avoid infinite recursion when the cumulative distribution
     function does not converge to 1.

   o Function cm() gains a 'maxit' argument for the maximum number of
     iterations in pseudo-estimators calculations.

   o Methods of aggregate(), frequency(), severity() and weights() for
     objects of class "simpf" gain two new arguments:

     1. 'classification'; when TRUE, the columns giving the
        classification structure of the portfolio are excluded from the
        result. This eases calculation of loss ratios (aggregate claim
        amounts divided by the weights);

     2. 'prefix'; specifies a prefix to use in column names, with
         sensible defaults to avoid name clashes for data and weight
         columns.

BUG FIXES

   o The way weights had to be specified for the "chi-square" method of
     mde() to give expected results was very unintuitive. The fix has
     no effect when using the default weights.

   o The empirical step function returned by the "recursive" and
     "convolution" methods of aggregateDist() now correctly returns 1
     when evaluated past its largest knot.

DEPRECATED

   o Direct usage of bstraub() is now deprecated in favor of cm(). The
     function will remain in the package since it is used internally by
     cm(), but it will not be exported in future releases of the
     package. The current format of the results is also deprecated.


---
   Vincent Goulet, Associate Professor
   École d'actuariat
   Université Laval, Québec
   Vincent.Goulet at act.ulaval.ca   http://vgoulet.act.ulaval.ca