similar to: installing gzip files

I previously asked a question about performing archetypal analyis in R. In addition to the responses I received from r-help, the author sent me some S code to peform the analysis. With the author's permission, I ported the S code to R using two excellent sets of instructions: (1) From the FAQ - 5.5 How can I create an R package? (2) Thomas Lumley's instructions on porting S to R -

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

Dear all, Version 4.3-0 of the randomForest package is now available on CRAN (in source; binaries will follow in due course). There are some interface changes and a few new features, as well as bug fixes. For those who had used previous versions, the important things to note are: 1. there's a namespace now, and 2. some functions have been renamed. The list of changes since 4.0-7 (last

A new version of the randomForest package is now available on CRAN. The DESCRIPTION is: Package: randomForest Title: Breiman's random forest for classification and regression Version: 3.4-1 Depends: R (>= 1.5.0) Author: Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener. Description: Classification and regression based on a forest of trees using

We are organizing three projects to honor Leo Breiman this year. First, Salford Systems' Data Mining Conference will open with a special session devoted to his scientific contributions. Second, we would like to construct Leo's scientific "family tree," identifying Leo's students, collaborators, and others whose work has been influenced in an important way by Leo's

We are happy to inform you that the online abstract submission and registration for `useR! 2009' is now available online from http://www.R-project.org/useR-2009/ This meeting of the R user community will take place at Agrocampus, Rennes, France, July 8 to 10, 2000. The conference schedule comprises invited lectures and user-contributed sessions as well as half-day tutorials presented by

We are happy to inform you that the online abstract submission and registration for `useR! 2009' is now available online from http://www.R-project.org/useR-2009/ This meeting of the R user community will take place at Agrocampus, Rennes, France, July 8 to 10, 2000. The conference schedule comprises invited lectures and user-contributed sessions as well as half-day tutorials presented by

Dear members of the R user community, the submission deadline for useR! 2009 (Rennes, France, July 8 to 10, 2009) is approaching: Submission deadline: 2009-02-27 (and along with that the early registration deadline). We encourage you to give a presentation at the conference and submit an abstract or a poster on topics presenting innovations or exciting applications of R. You can use the

Dear members of the R user community, the submission deadline for useR! 2009 (Rennes, France, July 8 to 10, 2009) is approaching: Submission deadline: 2009-02-27 (and along with that the early registration deadline). We encourage you to give a presentation at the conference and submit an abstract or a poster on topics presenting innovations or exciting applications of R. You can use the

Hello R-devel: I have written a package, called "woods", that does classification trees (R function CT), and currently, only the most basic functionality of Random Forest, e.g. bagged trees with choices about sample size, with/without replacement, size of (random) subset of covariates drawn when nodes are split. My reason for writing this is twofold. First, I wanted to base this

DATA MINING 2006 CONFERENCE sponsored by Salford Systems San Diego, California: March 30 - March 31, 2006 (with a Welcome Reception on March 29th at 6PM) Two Full Days of Real-World Case Study Presentations Schedule: www.salforddatamining.com/docs/schedule06.pdf <http://www.salforddatamining.com/docs/schedule06.pdf> *Conference Highlights:* * State-of-the-Art Research from Leading

While attempting to package up the HEAD version of nut for our department/campus, I've run into a couple of minor issues with the latest HEAD - I'm going to give it another whack at a PW9170+ UPS and want to make sure I have the latest revision. :) First is, whilst building the drivers (SNMP required), I get: gmake[1]: Entering directory `/fsys2/source/nut-2.1/nut-2.1_cvs/drivers'

I would like to be able to randomise presence-absence (i.e. binary) matrices whilst keeping both the row and column totals constant. Is there a function in R that would allow me to do this? I'm working with vegetation presence-absence matrices based on field observations. The matrices are formatted to have sites as rows and species as columns. The presence of a species on a site is

I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion? Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user.

dev.new(width=6, height=1.5,mar=c(0,0,0,0)) par(mfrow=c(1,1),mar=c(.5, .5, 1.5, .5), oma=c(.4, 0,.5, 0)) barplot(c(1,1,1,1,1,1),col=c("blue","purple","red","green","orange","yellow"), axes = FALSE) I have a barplot that returns six colors in a line. I would like to get the same six color blocks in a hexagram layout (if it were a clock,

I"m sorry if this is not the correct place for this question but i have not been able to locate any documentation on the mem=size option for memdisk The only thing i have found is mem=size Mark available memory above this point as Reserved. But i'm not sure if this is accurate and i'd like to understand the purpose of this option I am PXE booting a LENOVO machine which

Hi, I am having some trouble using R 2.13.1 for generating a pmml object of of class "c('randomForest.formula', 'randomForest')" I see that these methods are available: > methods(pmml) [1] pmml.coxph* pmml.hclust* pmml.itemsets* pmml.kmeans* pmml.ksvm* pmml.lm* pmml.multinom* pmml.nnet* pmml.rpart* [10] pmml.rsf* pmml.rules* pmml.survreg*

Hi, Sorry for not being specific. I just wanted to know if there is any way at all to force clang to generate intermediate code for static functions when they are not being called anywhere inside the current module. Other compilers seem to generate intermediate code (lcc, for instance). Thanks for your reply..Olivier. Sincerely Arvind On Mon, Dec 14, 2009 at 1:10 PM, Olivier Meurant

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the