similar to: Major update of package actuar

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

=== 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

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

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

Dear R users, I am trying to minimize the distance between my data points and theoretical gamma distribution over shape and scale parameters. the function "mde" from actuar package does it for empirical distribution function and theoretical gamma distribution. However, I would like to minimize the distance by using only the data between 0.1 and 0.9 quantiles. I cannot use ecdf in this

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of Science in Statistics at Mindanao State University-Iligan Institute Technology (MSU-IIT), Philippines. I am currently working on my master's thesis titled "Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain

I'm trying to do a sort of home-brew item-characteristic-curve. This is a plot of the probability of getting a test item correct, as a function of the mean score on the test. (The last part is the home brew part.) Logistic regression with glm would work nicely, EXCEPT for the fact that the curve requires a guessing parameter. So the asymptote on the left is not 0 but rather something like

I know of no existing functions for estimating the parameters of this model using MCMC or MML. Many years ago, I wrote code to estimate this model using marginal maximum likelihood. I wrote this based on the using nlminb and gauss-hermite quadrature points from statmod. I could not find that code to share with you, but I do have code for estimating the 3PL in this way and you could modify the

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

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

Hi just wondering if there is a package that can get the maximum likelihood or method of moments estimator for distributions with censored data? The distributions I'm interested in are: Exponential, pareto, beta, gamma and lognormal. -- View this message in context: http://www.nabble.com/MLE-for-censored-distributions-in-R-tp15022863p15022863.html Sent from the R help mailing list archive at

Dear R-Experts, I was wondering how to fit a cumulative gaussian to a set of empirical data using R. On the R website as well as in the mail archives, I found a lot of help on how to fit a normal density function to empirical data, but unfortunately no advice on how to obtain reasonable estimates of m and sd for a gaussian ogive function. Specifically, I have data from a psychometric function

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

Hi, how can I treat data organised in classes and frequencies? Ex. class frequency 20-23 9 23-25 7 26-28 5 29-31 5 32-34 3 Thanks Angelo Scozzarella

I just (apparently) figured out how to do the stuff described in Section 5.4.2 of Writing R Extensions. I put my test toy packages on github <https://github.com/cjgeyer/linkingTo> for anyone to copy. If anyone cares to read the README and the bits of code it links to and tell me anywhere I am wrong, I would be grateful. But the main point of this e-mail is a complaint about that section