similar to: randomForestSRC 2.9.0 is now available

Hello, I am wanting to use MRF to do multivariate regression. We are testing whether acoustic indices can predict structure (relative abundances) of vocalising avian community in UK and Ecuador. There are 26 acoustic indices, 65 UK species and 95 Ecuadorian species. I want to build a model for each ecozone (UK/ EC) using all species (relative abundance) as response matrix, and acoustic indices as

Dear useRs: The latest build of randomForestSRC (2.5.1) includes a configuration file that switches on the -fopenmp tag during compilation on systems that support OpenMP parallel processing. Previously, to achieve parallel processing, we required the additional step of executing an autoconf command before compiling and installing the package from source. We also believe the binaries provided by

Dear useRs: Release 3.2.0 of the CRAN package randomSurvivalForest is now available. ---------------------------------------------------------------------------------------------- Release 3.2.0 represents a significant upgrade in the functionality of the product. Key changes are as follows: o A second method of perturbing the data set in order to calculate variable importance (VIMP) has been

Dear useRs: Release 3.2.0 of the CRAN package randomSurvivalForest is now available. ---------------------------------------------------------------------------------------------- Release 3.2.0 represents a significant upgrade in the functionality of the product. Key changes are as follows: o A second method of perturbing the data set in order to calculate variable importance (VIMP) has been

We recently posted randomForestSRC on CRAN. It uses OpenMP in the native code extensively. We set the default number of cores to two (2), but we typically run it at the maximum (omp_get_max_threads()) during analysis. Currently, users need to set options(), set an environment variable, or edit their .Rprofile to use more than two cores. We followed the protocol for mc.cores in mclapply() in

Please find release 3.6.0 of the package "randomSurvivalForest" now available for download on CRAN. --------------------------------------------------------------------------------- CHANGES TO RELEASE ?3.6.0 RELEASE 3.6.0 represents the last and final major upgrade of this product. ?Current and future functionality will migrate to the new CRAN package, Random Forests for Survival,

Please find release 3.6.0 of the package "randomSurvivalForest" now available for download on CRAN. --------------------------------------------------------------------------------- CHANGES TO RELEASE ?3.6.0 RELEASE 3.6.0 represents the last and final major upgrade of this product. ?Current and future functionality will migrate to the new CRAN package, Random Forests for Survival,

Hi, I am writing this message for the benefit of the community.I have been working with randomForestSRC on a windows machine.I have been trying for days to output the forest to an rfz file so that I can print a graphical representation of the tree using Ishwaran and Kogalur's java GUI for visualizing forests at no avail.I finally contacted Dr. Kogalur who has been extremely helpful.The bottom

Hi I use pixmapIndexed tmp.vimp <- array(0,c(x.dim,y.dim)) tmp.vimp <- pixmapIndexed(tmp.vimp, col=rainbow) to plot values of a 2D matrix. I 'fill' the pixmapIndexed like: for (x in 1:x.dim) { for (y in 1:y.dim) { tmp.vimp at index[x,y] <- my.matrix[x,y] }} how can I define, that the colors are painted e.g. according the rainbow

Max and List, Could you advise me if I am using the proper caret syntax to carry out leave-one-out cross validation. In the example below, I use example data from the rda package. I use caret to tune over a grid and select an optimal value. I think I am then using the optimal selection for prediction. So there are two rounds of resampling with the first one taken care of by caret's train

Apologies for any obtuseness in the following. We have been working on Version 2.0 of the randomSurvivalForest CRAN package and we're encountering a perplexing 'memory not mapped' segfault that we believe is "influenced" by GC. We essentially have two R functions, rsf.default(..), and predict.rsf(..) and two corresponding entry points, rsfGrow(...), and rsfPredict(...),

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Please find release 1.0.0 of the new CRAN package "spikeslab" now available for download. Thank you. --------------------------------------------------------------------------------- RELEASE 1.0.0 is the first and initial release of this package. Fits a rescaled spike and slab model using a continuous bimodal prior. Can be used for prediction and variable selection in low and

Please find release 1.0.0 of the new CRAN package "spikeslab" now available for download. Thank you. --------------------------------------------------------------------------------- RELEASE 1.0.0 is the first and initial release of this package. Fits a rescaled spike and slab model using a continuous bimodal prior. Can be used for prediction and variable selection in low and

Please find release 1.1.0 of the CRAN package "spikeslab" now available for download. Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection. CHANGES TO RELEASE 1.1.0 RELEASE 1.1.0 is a recommended upgrade of the product. o cv.spikeslab() now takes advantage of the CRAN package "snow". It

Please find release 1.1.0 of the CRAN package "spikeslab" now available for download. Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection. CHANGES TO RELEASE 1.1.0 RELEASE 1.1.0 is a recommended upgrade of the product. o cv.spikeslab() now takes advantage of the CRAN package "snow". It

Please find release 3.5.0 of the CRAN package "randomSurvivalForest" now posted on CRAN. Thank you. ubk2101 at columbia.edu Udaya B. Kogalur, Ph.D. Kogalur Shear Corporation 5425 Nestleway Drive, Suite L1 Clemmons, NC 27012 --------------------------------------------------------------------------------- CHANGES TO RELEASE 3.5.0 RELEASE 3.5.0 represents a significant upgrade in the

Please find release 3.5.0 of the CRAN package "randomSurvivalForest" now posted on CRAN. Thank you. ubk2101 at columbia.edu Udaya B. Kogalur, Ph.D. Kogalur Shear Corporation 5425 Nestleway Drive, Suite L1 Clemmons, NC 27012 --------------------------------------------------------------------------------- CHANGES TO RELEASE 3.5.0 RELEASE 3.5.0 represents a significant upgrade in the

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and