similar to: New version of actuar

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

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

=== actuar: An R Package for Actuarial Science === We are pleased to announce the immediate availability of version 1.0-0 of actuar. This release follows publication of our papers in JSS (*) and R News (**). From the NEWS file: Version 1.0-0 ============= NEW FEATURES o Improved support for regression credibility models. There is now an option to make the computations with the

=== actuar: An R Package for Actuarial Science === We are pleased to announce the immediate availability of version 1.0-0 of actuar. This release follows publication of our papers in JSS (*) and R News (**). From the NEWS file: Version 1.0-0 ============= NEW FEATURES o Improved support for regression credibility models. There is now an option to make the computations with the

I run the following: library(actuar) x <- seq(0, 22, 0.5) fl <- discretize(plnorm(x, 2.1), from = 0, to = 22, step = 0.5, method ="lower") Fs <- aggregateDist("recursive", model.freq = "poisson",model.sev = fl, lambda = 10, x.scale = 0.5) Warning message: In panjer(fx = model.sev, dist = dist, p0 = p0, x.scale = x.scale, : maximum number of recursions

Dear useRs, I'm happy to announce a substantial update of package actuar that bumps the version number to 2.0-0. This release focuses on additional support for continuous and discrete distributions, new functions to simulate data from compound models and mixtures, and revised and improved documentation. A slightly shortened version of the NEWS file follows: NEW FEATURES ? Support for the

Dear useRs, I'm happy to announce a substantial update of package actuar that bumps the version number to 2.0-0. This release focuses on additional support for continuous and discrete distributions, new functions to simulate data from compound models and mixtures, and revised and improved documentation. A slightly shortened version of the NEWS file follows: NEW FEATURES ? Support for the

Dear useRs, A new version of actuar is available since last Friday. This is mainly a bugfix release. From the NEWS file: Version 1.0-2 ============= USER-VISIBLE CHANGES o m<foo>() and lev<foo>() now return Inf instead of NaN for infinite moments. (Thanks to David Humke for the idea.) BUG FIXES o Non-ascii characters in one R source file prevented compilation of the package in

Dear useRs, A new version of actuar is available since last Friday. This is mainly a bugfix release. From the NEWS file: Version 1.0-2 ============= USER-VISIBLE CHANGES o m<foo>() and lev<foo>() now return Inf instead of NaN for infinite moments. (Thanks to David Humke for the idea.) BUG FIXES o Non-ascii characters in one R source file prevented compilation of the package in

Hola, Estoy intentando esta convolución con una discretizada Gamma con Binomial negativa pero me da el siguiente error. Alguien sabe como puedo hacer funcional este algoritmo con estos parámetros? > fx<-discretize(pgamma(x,shape=2.4149,scale=5742.2), + method="rounding",from=0, to =100, step=0.5) > Fs <- aggregateDist("recursive", model.freq = "negative

Hi, I would like to use the following codes to plot the CDF for pareto distribution. Before doing this, I have plot the emperical one. x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4477557,), col="red") Could anyone give me some advice whether the above codes are correct? Many thanks. -- View this message in context:

I need to find a computationally simple process for the movement of interest rates. In this simplified model, an interest rate can have 3--5 possible values, and its movement is characterized by a matrix of transition probabilities (ie, it is a Markov process). I would like to estimate this process from a given set of data. For example, let the interest rate time series be: 7 3 8 2 5 9 6

Hi R-help, Is there such a thing as a function in R for fitting a GLM where the response is distributed as a Beta distribution? In my case, the response variable is a percentage ([0,1] and continuous). The current glm() function in R doesn't include the Beta distribution. Thank you for any help on this topic. Sincerely, Sharon K?hlmann

Hello. I am not certain even how to search the archives for this particular question, so if there is an obvious answer, please smack me with a large halibut and send me to the URLs. I have been experimenting with fitting curves by using both maximum likelihood and maximum spacing estimation techniques. Originally, I have been writing distribution-specific functions in 'R' which work

Hello all, I would like to plot the emperical CDF, normal CDF and pareto CDF in the same graph and I amusing the following codes. "z" is a vector and I just need the part when z between 1.6 and 3. plot(ecdf(z), do.points=FALSE, verticals=TRUE, xlim=c(1.6,3),ylim=c(1-sum(z>1.6)/length(z), 1)) x <- seq(1.6, 3, 0.1) lines(x,pgpd(x, 1.544,0.4373,-0.2398), col="red") y

Dear list, Can anyone provide a package or code for estimating the parameters of a mixture of c (c >=2) univariate normal distributions? I've tried the algorithm provided by Venables & Ripley (1999) p 263, for the mixture of two normal, but I don't find the "ms" function in R. I've used nls instead, but I'm not sure if it works the same. The data I have is very

Dear All, I am trying to generate some Pareto random variates using the inverse method. This is really straightfoward and my R function looks as below : pareto <- function(c, a, cnt=1000) { u <- runif(cnt) x <- (c / ((u ^ (1 / a)))) mean.theo <- ((c * a) / (a - 1)) mean.gen <- mean(x) cat('Pareto mean : theoritical', mean.theo, 'generated', mean.gen,

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

------------------------------------------------------------------------------- New versions released for the distr family of package ------------------------------------------------------------------------------- We would like to announce the availability on CRAN (with possibly a minor delay until on every mirror) of new versions of our packages in the "distr"-family (version 2.1),

Dear all (copying the package author), I have a question on the vlmc package. I am trying to model a time series, where each element can take one of 11 values (the result of some clustering). When I run the following command (synthetic data to facilitate self-contained example) I get the following warning: ("alphabet with >1-letter strings; trying to abbreviate") +++ START+++ >