similar to: idea for GSoC: an R package for fitting Bayesian Hierarchical Models

Dear R-users (and developers), I am looking for an efficient framework to carry out parameter estimations based on MCMC (optionally with specified priors). My goal is as follow: * take ANY R-function returning a likelihood-value (this function may itself call external programmes or other code!) * run a sampler that covers the multidimensional parameter space (thus creating a posterior

Hi all, I've managed to get JAGS working on my Ubuntu Hardy Linux with a 32-bit computer and AMD processors using R 2.8.1. JAGS is great. I've read that JAGS is the fastest, but that hasn't been my experience. At any rate, I have more experience with WinBUGS under Windows and would like a version of that working as well. It seems like I've read a lot on the subject and tried a

Hello ALL! Some times ago I started to learn and play with Bayesian stuffs. Many advice use of WinBUGS for Bayesian inference Using Gibbs Sampler. However, WinBUGS is discontinued, and now, development is under OpenBUGS. I wasn't lazy, so I installed both and tried out. In more than 90% of cases they give comparable outcome. But in few cases I got substantial differences. Recently, I read nice

Frank (or anyone else), can you offer any comments comparing runjags, R2jags, rjags ? I couldn't find any vignettes, nothing except a brief mention on Task Views. Kevin On Wed, Oct 7, 2009 at 7:48 AM, Frank E Harrell Jr <f.harrell@vanderbilt.edu > wrote: > Have you tried the rjags package which uses the jags system? It is much > more integrated into R and works quite well.

Hello, I recently changed from Win XP to Mac OS X (10.5.1). Is there a way to run Bugs (in any version) in R (R version 2.6.0 (2007-10-03)) on this platform? Fredrik

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Hello. In this post: http://finzi.psych.upenn.edu/Rhelp10/2010-March/233815.html Uwe Ligges suggests using BRugs rather than R2WinBUGS under windows. He also notes that it is not in the main CRAN repository, but it is in "extras" which is a default repository under windows. I have OpenBUGS 3.1.0 (the latest that has a native Linux version which is no longer called linBUGS)

I'm back! I've just learned that, on a fully updated Fedora Core Linux5 sytem, the working solution to access Winbugs under wine via the R package "rbugs" no longer works. Here was my last post on this topic (with the formerly working solution) from January. http://finzi.psych.upenn.edu/R/Rhelp02a/archive/68497.html Currently, what happens is that WinBUGS starts up, but just

I have found myself doing a large amount of Bayesian analysis and I am uncertain what framework learn and I'm posing this question to get a sense of perspective. As I am doing a lot of applied work right now, I need to do a fair amount of "standard data analysis" (eg mixed models, glm, etc) but I also have to have the flexibility to write more complicated models. As near as I can

Hello ALL! I am running Linux, Fedora 15 64-bits, and R on it. I need to use WinBUGS and R2WinBUGS, but as far as I read, WinBUGS is closed project, to be continued with/as OpenBUGS. Thus, I have found R2OpenBUGS on OpenBUGS Contributed Code (http://openbugs.info/w/UserContributedCode), not on CRAN. Author(s) states that it is equivalent for R2WinBUGS. I tried briefly, and realized few minor

I'm using the dump command to pass data to WinBUGS/OpenBUGS/JAGS and have run into a problem. Here is some R-code: foo <- array(1:6, dim=c(2,3)) dump('foo', file='dumpdata.R') dump('foo', file='dumpdata.R', append=TRUE, evaluate=TRUE) foo2 <- array(c(2,3,5,7,9,7,5,3), dim=c(2,4)) dump('foo2', file='dumpdata.R', append=TRUE) And here is

Dear R-helpers, Hi! All. I'm doing a project which needs MCMC simulation. I wonder whether there exists related packages in R. The only one I know is a MCMCpack package. What I want to do is implementing gibbs sampling and Metropolis-Hastings Algorithm to get the posterior of hierarchical bayesian models. Thanks in advance. Jun

(Moved from r-devel to r-bugs) On 3/24/2006 5:03 AM, Antonio, Fabio Di Narzo wrote: > Hi all. > There's a bug in plot.acf, when plotting acf for multivariate time series. > Here a reproducible example: > > X <- rnorm(1000) > Y <- -X + rnorm(1000, sd=0.6) > Z <- cbind(X,Y) > > In > acf(Z) > cross-correlation plot y-axis is limited to 0-1. But: >

Hi All, I am trying to figure out how to use R-Coda with the output from OpenBugs. I have installed and loaded the packages BRugs and R2WinBUGS. I have successfully run a simple Bayes model in WinBUGS using R2WinBUGS' "bugs" and have used "read.bugs" to build the coda object. I can successfully switch to OpenBugs and run the same model and get the basic summary

This code gives an error: a <- ts(1:10, start=0, freq=10) b <- ts(1:10, start=1, freq=10) ts.intersect(a,b) This one works normally (and correctly): a <- ts(1:10, start=0) b <- ts(1:10, start=1) ts.intersect(a,b) Antonio, Fabio Di Narzo. P.S. How to switch off italian error messages to post on r-help? > version _ platform i386-pc-mingw32 arch i386 os

Hi all, I observed that the 'link' between R and BUGS (winBugs/linBugs) has totally disappeared. I'm wondering what package can be use to run bayesian models specified using the BUGS language in R (specifically under Linux). Is there any other option besides JAGS ? Thanks ----------------------------------------- Stay ahead of the information curve. Receive EDA news and jobs on

Hi, I am running a model with count data and one categorical predictor (simple model for me to understand it fully), I did in R a glm like this: glm(Recruitment~Depth, family=poisson). I get the coefficientes and confidence intervals and all is ok. But then I want to do the same model with Bayesian stats, here is my code: model { for (i in 1:232) { Recruitment[i]~dpois(lambda[i])

I've enconuntered this problem with the last cran version of geoR: > library(geoR) > day <- rep(1:2, each=5) > coords <- matrix(rep(runif(10),2), 10, 2) > data <- rnorm(10) > data[1] <- NA > as.geodata(cbind(coords, data, day), realisations=4) as.geodata: 1 points removed due to NA in the data Errore in as.geodata(cbind(coords, data, day), realisations = 4) :

Full_Name: Antonio, Fabio Di Narzo Version: 2.6.1 OS: linux Submission from: (NULL) (213.140.16.187) In 'outer' man page, there is no mention of the Vectorize function. Moreover, I think it isn't underlined enough that the FUN argument to 'outer' must be a vectorized function (doc speaks about a function which has to 'operate elementwise'). A cross-reference from

Hello, I am trying to install rjags on R 3.0.0 that is running on CentOS 6.4. Jags is installed and seems to be working correctly. I get the following error when trying to install from source or CRAN, configure: error: "Problem with header file /usr/include/JAGS/Console.h " See `config.log' for more details JAGS was intalled with an rpm. Does not look like the rpm included