similar to: GC encountered a node (…) with an unknown SEXP type

I'm trying to resolve a problem of Namespaces and probably have misunderstood something basic. In my quantreg package the function crq() has as its first argument a formula that typically contains something like this: Surv(y, ...) ~ foo, where Surv is a function from the survival package which quantreg suggests. When this syntax is encountered in ordinary usage Surv is resolved, I believe,

Hello, For the BUILTINSXP and SPECIALSXP types, the R Internals page documents "An integer giving the offset into the table of primitives/.Internals. " What macro gives me the value of this integer? I guess something like this would work had R_USEINTERNALS been defined v->u.primsxp.offset (where v is a SEXP of either of the above types). What is the portable version? Thank you

Is it possible to define an external special function (SPECIALSXP)? I'm trying to do some language-level work, and don't want my arguments evaluated before they hit C. It looks like the only way to define a SPECIALSXP is by using XX0 in the `eval' field of R_FunTab; is there any way to make this applicable to externally defined functions?

Full_Name: Ed Borasky Version: 1.8.1 OS: Windows XP Professional Submission from: (NULL) (208.252.96.195) R 1.8.1 seems to be running into a memory allocation problem in the "aggregate" function. I have a rather large dataset (14 columns by 223,000 rows -- almost 40 megabytes) and a script that performs some processing on it. The system is a 768 MB Pentium 4. Here's the console

Hi, Thanks. Can you elaborate? Thanks. Dave. On 3/6/18, Steffen Kaiser <skdovecot at inf.h-brs.de> wrote: > -----BEGIN PGP SIGNED MESSAGE----- > Hash: SHA1 > > On Fri, 2 Mar 2018, David Mehler wrote: > >> dovecot to require a complete email address as a login. Currently I >> can log in by either a username or fqdn. I've got the below what is >> the

[some de-capitalization of *SXP done manually by mailing list maintainer ; the originally was caught as potential spam. MM] I have a little R program that crashes with the message xmalloc: out of virtual memory The code has a repeat{} loop that watches the sizes of some files. When there's an increase it updates things by reading the last 65 lines of each file, doing some

Does any have installation instructions for this? When I run install.packages('quantreg') I get: gcc -std=gnu99 -shared -o quantreg.so akj.o boot.o brute.o chlfct.o cholesky.o combos.o crq.o crqfnb.o dsel05.o etime.o extract.o idmin.o iswap.o kuantile.o mcmb.o penalty.o powell.o rls.o rq0.o rq1.o rqbr.o rqfn.o rqfnb.o rqfnc.o sparskit2.o srqfn.o srqfnc.o srtpai.o -llapack -lblas

In help(gc) I read, "...the primary purpose of calling 'gc' is for the report on memory usage". What memory usage does gc() report? And more importantly, which memory uses does it NOT report? Because I see one answer from gc(): used (Mb) gc trigger (Mb) max used (Mb) Ncells 14875922 794.5 21754962 1161.9 17854776 953.6 Vcells 59905567 457.1 84428913 644.2

This includes two kernel patches, scsi-target and IO_CMD_EPOLL_WAIT. The former is a modified version of the scsi target infrastructure in mainline. The latter enables applications to handle AIO and non-AIO fds in the same loop. blktap uses the different patch, AIO event queue patch for the same aim. The IO_CMD_EPOLL_WAIT patch will be merged into mainline (and the AIO event queue will not) so

Happy new year to all; A few days ago, I posted similar problem. At that time, I found out that our R program had been 32-bit compiled, not 64-bit compiled. So the R program has been re-installed in 64-bit and run the same job, reading in 150 Affymetrix U133A v2 CEL files and perform dChip processing. However, the memory problem happened again. Since the amount of physical memory is 64GB, I think

Hi, In Rcpp, we now have a "Function" class to encapsulate functions (they cover all three kinds, but this may change). To call the function, what we do is generate a call with the function as the first node and then evaluate the call. SEXP stats = PROTECT( R_FindNamespace( mkString( "stats") ) ); SEXP rnorm = PROTECT( findVarInFrame( stats, install( "rnorm") ) )

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

> > On 7 Oct 2019, at 13:47, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > On 07/10/2019 4:22 a.m., Lionel Henry wrote: >> Hi Gabe, >>> There is another way the pipe could go into base R that could not be >>> done in package space and has the potential to mitigate some pretty >>> serious downsides to the pipes relating to debugging

With reference to R News News: R version 3.0.0 (Masked Marvel) has been released on 2013-04-03. R version 2.15.3 (Security Blanket) has been released on 2013-03-01 R version 2.15.2 (Trick or Treat) .... R version 2.15.1 ("Roasted Marshmallows") ... R version 2.15.0 ("Easter Beagle") R version 2.14.0 ("Great Pumpkin") Dear R help List, How are these version types

On Thu, Jan 22, 2015 at 11:44 AM, <luke-tierney at uiowa.edu> wrote: > I'm not convinced that how to make :: faster is the right question. If > you are finding foo::bar being called often enough to matter to your > overall performance then to me the question is: why are you calling > foo::bar more than once? Making :: a bit faster by making it a > primitive will remove

Dear all, I have written a little R program to convert images. See below. Within the loop over j (the filenames) memory consumption grows constantly. rm( ... ) inside the loop did not help. Memory does not grow if I remove the writeBin statements between the two #-------- marks. But obviously this is not solution I want... Thanks for any advice. Manfred Baumstark P.S. As I'm new to R:

On Thu, Jan 22, 2015 at 11:44 AM, <luke-tierney at uiowa.edu> wrote: > > For default methods there ought to be a way to create those so the > default method is computed at creation or load time and stored in an > environment. We had considered that, but we thought the definition of the function would be easier to interpret if it explicitly specified the namespace, instead of

Hi, all; I know there has been a lot of discussions on memory usage in R. However, I have some odd situation here. Basically, I have a rare opportunity to run R in a system with 64GB memory without any limit on memory usage for any person or process. However, I encountered the memory problem error message like this: Error: cannot allocate vector of size 594075 Kb I got this error message while

I start by doing a simple gaussian tobit by MLE: x1 <- runif(1000) # E() = 0.5 x2 <- runif(1000)*2 # E() = 1 x3 <- runif(1000)*4 # E() = 2 ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0 y <- ystar censored <- ystar <= 0 y[censored] <- 0 library(AER) m <- tobit(y ~ x1 + x2, left=0, data=D) summary(m) Which gives: Call:

Hi r-devel, I am working on a R extension. I am writing the function in C++, and in my function it is required a R function object from the user. This R function object will be evaluated thousand of times in my C++ code. I generated the shared library and I loaded it on R. I did several experiments in order to compare the speed of my compiled code vs the speed of the equivalent interpreted code.