similar to: Algoritmo de Panjer

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

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

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

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

We obtained some disturbing results from convolve() (inaccuracies and negative probabilities). We'll try to make the context clear in as few lines as possible... Our function panjer() (code below) basically computes recursively the probability mass function of a compound Poisson distribution. When the Poisson parameter lambda is very large, the starting value of the recursive scheme ---

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

=== 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

Sample Size λ=5      α = 4       β = 3 Min. 1St Qu. Median Mean 3rd Qu. Max. 100 0.000000 1.740638 4.040032 4.433828 5.607589 22.450405 500 0.000000 2.212375 3.915889 4.750014 6.356894 22.860806 1000

Michael thank you very much, what about the options I have tried all without success. Try again to send a message to the list by changing the subject line. Merry Christmas Juan -- ========================================================= Juan Antonio Gil Pascual Prof. Titular de M?todos de Investigaci?n en Educaci?n correo: jgil en edu.uned.es web: www.uned.es/personal/jgil U.N.E.D. Fac. de

Cordial saludo para cada uno. De manera amable les pido ayuda para acceder al código R usado para el algoritmo de la función qnorm. Gracias por su ayuda. César Escalante C. [[alternative HTML version deleted]]

Hola Alguien me podría decir como hacer una grafica del tipo persp() de la densidad de una distribucion normal bivariante estandarzada con correlacion 0.5?? gracias [[alternative HTML version deleted]]

Hola, ¿Alguien sabe cómo lidia la función aov con diseño no balanceado? ¿Utiliza el las funciones de suma de cuadrados type II ? Gracias!! [[alternative HTML version deleted]]

Una distribución binomial negativa podría ser adecuada. De hecho, una extensión de la binomial negativa, como la distribución de Waring generalizada, termina convergiendo a una distribución binomial negativa. Puedes ver que la mejora en la bondad del ajuste de la binomial negativa con respecto a la Poisson es sustancial (te muestro también las salidas.). datos<-c(1280, 1262, 1290, 1321,

Hola, Quiero saber si es posible programar una cópula donde las funciones marginales son multivariantes, siguiendo el esquema del package ''Copula''. Es decir, Copula(F(x,y), G(w,z))) En el caso de funciones marginales univariantes, un ejemplo de la normal multivariante quedaria de la siguiente forma,

Dear All, A simple question: packages like fitdistr should be ideal to analyze samples of data taken from a univariate distribution, but what if rather than the raw data of the observations you are given directly and only a histogram? I was thinking about generating artificially a set of data corresponding to the counts binned in the histogram, but this sounds too cumbersome. Another question is

Estimados, Les consulto por el siguiente problema. Variable respuesta: Número de picadas de alimentación de los insectos (picadas) Factores: 1) Especie de insecto (especie), 2) Estado de desarrollo de los insectos (edad) y 3) Estado de desarrollo de frutos (estado). Modelo: MLG con distribución binomial negativa como componente aleatorio. Estrategia de selección de modelo: Evaluación del cambio

Hi. I need to draw a circle whit center (a,b) and radio r. So I use the R code below a<-1.975 # valore x del centro b<-1.215 # valores y del centro r<-1.46 # radio x1<-seq(a-r,a+r,by=0.01); #los valores de x yp<-sqrt(r^2-(x1-a)^2)+b; # los valores y a partir de la ra??z positiva yn<-(-1)*sqrt(r^2-(x1-a)^2)+b; # los valores y a partir de la ra??z negativa x<-c(x1,x1);