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Have UI that simplifies running code from another (internal) package, allowing user to set values on fields I basically grabbed from results of calls to formals() for various functions. That works fine for the most part. But it was requested to investigate some type of popup help or something for each field. Since I already wrote the information in the dot-Rd files for what should be displayed,

What a delicious example! (I'm taking the liberty of sharing it with r-devel, since it raises some good issues.) You have two questions, presumably: 1 - how could the order of the setMethod calls make a difference in the results? 2 - what's causing the infinite loop & how could it be avoided, reliably? Second question first. The danger sign is the "vector" method:

I'm attempting to reflect the information for use with corresponding fields in GUI (in a different package), to provide default values, argname as key for UI label lookups, etc. So I want something much more like the formals of the implementation: { "object", "method": c("median", "vs", "tukey"),

As I was running "R CMD check" on one of my older packages (matlab), I was attempting to fix one of the NOTEs which bitches about attempting to change the search path using require(). In my case, I am repeating a package dependency for "methods" package in my .onLoad() function. Can this scenario (based on something B. Ripley discussed many years ago) no longer occur? Just

I have two packages, one that does the actual work (SC) and the other a Tcl/Tk UI (SCUI) that invokes methods within the former. Within the SCUI's invocation method, I save an object returned from SC, the results of a long-running method. Now the object is completely described by the SC package. Unfortunately, any attempt to load the object (in a fresh R session) fails as below. R>

Sir, I want to apply mclapply() function for my analysis. So, I have to install multicore package. But I can not install the package. >install.packages("multicore") It gives that package multicore is not available. Can you help me? Regards, Suman Dhara [[alternative HTML version deleted]]

Hi, I want to parallelize some computations when it's possible on multicore machines. Each computation produces a big objects that I don't want to store if not necessary: in the end only the object that best fits my data have to be returned. In non-parallel mode, a single gloabl object is updated if the current computation gets a better result than the best previously found. My plan

Interrogating some (of my own) code in another package. >norm.meth <- getMethod("normalize", "MatrixLike") >message("str(norm.meth)") >str(norm.meth) >message("show(norm.meth at .Data)") >show(norm.meth at .Data) Last show() displays this: function (object, ...) { .local <- function (object, method = c("median",

On a Unix machine I ran caret::rfe using the multicore package, and I saved the resulting object using save(lm2, file = "lm2.RData"). [Reproducible example below.] When I try to load("lm2.RData") on my Windows laptop, I get Error in loadNamespace(name) : there is no package called 'multicore' I completely understand the error and I would like to ignore it and

How would one go about getting sprintf to use the values of a vector without having to specify each argument individually? > v <- c(1, 2, -1.197114, 0.1596687) > iv <- c(3, 1, 2, 4) > sprintf("%9.2f\t%d\t%d\t%8.3f", v[3], v[1], v[2], v[4]) [1] " -1.20\t1\t2\t 0.160" Essentially, desired effect would be something like: >

It is so difficult ... Which FE? It need BE support? I didn't get it. 2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the > back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at gmail.com> wrote: > >> Multicore? >> You

Hi all, I am new to this LLVM. I went through the documenation of LLVM but I didn't find any support for Multicore. Is there any such possibility where multicore architecture can be exploited using LLVM. Thanks Vijay -------------- next part -------------- An HTML attachment was scrubbed... URL:

On Jan 28, 2015, at 6:37 PM, Michael Lawrence <lawrence.michael at gene.com> wrote: > At this point I would just due: > > formals(body(method)[[2L]]) > > At some point we need to figure out what to do with this .local() confusion. Agreed, definitely. The current hack is to avoid re-matching arguments on method dispatch, so a fix would need to be fairly deep in the

What's the best way to simulate Recall for parent function? Consider this one-time recursive code: alwaysEven <- function(x) { handleOdd <- function(x) { alwaysEven(x-1) # use Recall-like here } return(if (x %% 2) handleOdd(x) else x) } any2even <- alwaysEven rm(alwaysEven) any2even(3) ---------------------------------------------------------- SIGSIG --

Can the value of a list element be referenced from a sibling list element during list creation without the use of a temporary variable? The following doesn't work but it's the general idea. > list(value = 2, plusplus = $value+1) such that the following would be the output from str() List of 2 $ value : num 2 $ plusplus: num 3

Dear R users, we have a problem when building R packages which depend on platform specific packages. The following example will illustrate our problem: For parallel computing (in our own package) we want to use the multicore package. Since multicore is not available for Windows we subtitute it by the snowfall package. Currently we create two packages with different "Depends" argument

On Aug 3, 2010, at 8:48 PM, Liu wrote: > It is so difficult ... > Which FE? It need BE support? I didn't get it. > > 2010/8/4 vijay kumar <vijaygbvv at gmail.com> > Yeah OpenMP support. I read that it has a front end support but not the back end. So are there any projects or teams looking at this issue. > > On Wed, Aug 4, 2010 at 7:24 AM, Liu <proljc at

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Hello All, In general when we use Rprof for performance evaluation on Multicore systems the output provides the time on the basis of the "user" time and the sampling time is equal to the the user time as reported by system.time. This does not seem right behavior when R is linked to BLAS/Lapack or other libraries which are optimized for parallel or multicore architectures as

Is there any reason to expect a problem ? i'm running this script on the cluster down the hall: module load R/2.11.0 R library(multicore) fxx<-function(ll) runif(1) mclapply(1:10,fxx) i get: Error in fork() : Unable to fork. less /proc/cpuinfo yields: processor : 0 vendor_id : GenuineIntel cpu family : 6 model : 15 model name : Intel(R) Xeon(R) CPU