similar to: NIMBLE package for hierarchical modeling now on CRAN

Dear R developers, these days I'm working on some R code for fitting completely generic Bayesian Hierarchical Models in R, a la OpenBUGS and JAGS. A key feature of OpenBUGS and JAGS is that they automatically build an appropriate MCMC sampler from a generic model, specified as a directed acyclic graph (DAG). The spirit of my (would-be) implementation is instead more focused on experimentation

Dear R users: I am using hierchical modeling for a response varible (ordinal) given by patients for each doctor. My MCMC run with MCMMCglmm ( thin=20,nitt=208000,burnin=24000, family='ordinal') still can't have all the parameters pass the GEWEKE diagnosis test. I am trying to find the reason for the slow convergence. Can the reason be 20 out of 50 doctors having 5 or less response

The latest version of `MCMCvis` is now available on CRAN. `MCMCvis` is an R package used to visualize, manipulate, and summarize MCMC output. MCMC output may be derived from Bayesian model output fit with JAGS, Stan, or other MCMC samplers. Improvements since the last CRAN release (0.8.1) include: * ability to calculate and plot prior posterior overlap * ability to return number of effective

The latest version of `MCMCvis` is now available on CRAN. `MCMCvis` is an R package used to visualize, manipulate, and summarize MCMC output. MCMC output may be derived from Bayesian model output fit with JAGS, Stan, or other MCMC samplers. Improvements since the last CRAN release (0.8.1) include: * ability to calculate and plot prior posterior overlap * ability to return number of effective

Is there a way to do hierarchical (bayesian) logistic regression in R, the way we do it in BUGS? For example in BUGS we can have this model: model {for(i in 1:N) { y[i] ~ dbin(p[i],n[i]) logit(p[i]) <- beta0+beta1*x1[i]+beta2*x2[i]+beta3*x3[i] } sd ~ dunif(0,10) tau <- pow(sd, -2) beta0 ~ dnorm(0,0.1) beta1 ~ dnorm(0,tau) beta2 ~ dnorm(0,tau) beta3 ~

Hi Friends, I'm trying to model the consumer decisions (Click-Through Rate and Conversion) in Search Engine Advertising using a hierarchical Bayesian binary logit. The input data is the weekly CTRs and Avg. Position for each search keyword. CTR is modeled as (for each keyword i and week j): Pij = exp(C + Bi x Positionij + A1 x Lengthi + A2 x Brandi + A3 x ProductSpecifici) / [1 + exp(C +

Looking at parLapplyLB, one sees that it takes in X and then passes splitList(X, length(cl)) to clusterApplyLB, which then calls dynamicClusterApply. Thus while dynamicClusterApply does handle tasks in a load-balancing fashion, sending out individual tasks as previous tasks complete, parLapplyLB preempts that by splitting up the tasks in advance into as many groups of tasks as there are cluster

When I run the gam function as part of an optimization and do the optimization many times using a loop, I'm finding that memory use increases over time (based on simply monitoring top). Below is some example code that involves varying the penalty parameter in gam, trying to find the value that gives exactly 50 edf for a simple smoothing problem. I thought I would post to the list to see if

Hi folks, A new release of Prawn is out, with some bug fixes and a couple backported features from edge. This release is API-compatible with 0.2.0, so please do upgrade! Release notes at: http://blog.majesticseacreature.com/archives/2008.10/prawn_0_2_1-2.html Enjoy! -greg -- Technical Blaag at: http://blog.majesticseacreature.com | Non-tech stuff at: http://metametta.blogspot.com

Hi, I'm trying to follow the R-admin instructions for using MKL10 as the external BLAS compiling R-2.6.2 under Linux on a RH EL head node of a cluster. The configure process seems to have problems when it checks for dgemm in the BLAS. I'm using configure as: ./configure CC=icc F77=ifort --with-lapack="$MKL" --with-blas="$MKL" where $MKL is defined as in R-admin

Dear R and hydrological/environmental community, A major release of hydroPSO is now available on CRAN. This new release: -) implements the Standard Particle Swarm 2011 (SPSO-2011) -) is parallel-capable, to alleviate the computational burden of complex models with "long" execution time -) has an improved performance (33% - 38% faster) -) allows normalization, when the parameter space

Dear R and hydrological/environmental community, A major release of hydroPSO is now available on CRAN. This new release: -) implements the Standard Particle Swarm 2011 (SPSO-2011) -) is parallel-capable, to alleviate the computational burden of complex models with "long" execution time -) has an improved performance (33% - 38% faster) -) allows normalization, when the parameter space

===== Summary ===== Version 0.1.0 provides the initial release of RcppSMC, an integration of the SMCTC template classes for Sequential Monte Carlo and Particle Filters (Johansen, 2009, J Statistical Software, 30:6) with the Rcpp package for R/C++ Integration (Eddelbuettel and Francois, 2011, J Statistical Software, 40:8). RcppSMC allows for easier and more direct access from R to the

===== Summary ===== Version 0.1.0 provides the initial release of RcppSMC, an integration of the SMCTC template classes for Sequential Monte Carlo and Particle Filters (Johansen, 2009, J Statistical Software, 30:6) with the Rcpp package for R/C++ Integration (Eddelbuettel and Francois, 2011, J Statistical Software, 40:8). RcppSMC allows for easier and more direct access from R to the

Sorry for the last email, sent too early. I have a small data set that has a hierarchical structure. It has both temporal (year, months) and spatial (treatment code and zone code). The following explains the data: WSZ_Code the water supply zone code (1 to 8) Treatment_Code the treatment plant which supplies each water supply zone (1 to 4)

CRAN (and crantastic) updates this week New packages ------------ * bdoc (1.0) Michael Anderson http://crantastic.org/packages/bdoc This package contains a function that will classify DNA barcodes as well as a few test and reference data sets. * bdsmatrix (1.0) Terry Therneau http://crantastic.org/packages/bdsmatrix This is a special case of sparse matrices, used by coxme and

Dear All, I hope I am not asking a FAQ. I am dealing with a problem of graph theory [connected components in a non-directed graph] and I do not want to rediscover the wheel. I saw a large number of R packages dealing for instance with the k-means method or hierarchical clustering for spatially distributed data and I am basically facing a similar problem. I am given a set of data which are the

So I've been an avid user of Left 4 Dead 1 and 2. This problem only seems to arise in L4D2, where the audio will cut out. Now, not completely, some things will drop but some stay, for instance, I can hear the noise my gun makes, the shots fired but I don't hear characters talking anymore or any special infected sounds. Another instance is I'll see my gun firing but I'll hear the

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 Everyone I have a MCMC loop to calculate a time varying hierarchical Bayesian structure. This requires me to use around 5-6 matrix inversions in the loop. I use cholesky and chol2inv for the matrix decomposition. Because of the data I am working with I am required to invert a 167 by 167 matrix twice in one iteration. I need to run the iteration for 10000 times, but I get the error