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I am trying to add the bigmemory packages but I get the following error message: In file included from bigmemory.cpp:14:0: ../inst/include/bigmemory/isna.hpp: In function 'bool neginf(double)': ../inst/include/bigmemory/isna.hpp:22:57: error: 'isinf' was not declared in this scope make: *** [bigmemory.o] Error 1 ERROR: compilation failed for package 'bigmemory' * removing

Hello All, I tried to intall the bigmemory package from a CRAN mirror site and received the following output while installing. Any idea what's going on and how to fix it? The system details are provided below. --------------------- begin error messages ----------------------- * installing *source* package 'bigmemory' ... checking for Sun Studio compiler...no checking for

Hi, I am trying to run the bigmemory example provided on the http://www.bigmemory.org/ The example runs on the "airline data" and generates summary of the csv files:- library(bigmemory) library(biganalytics) x <- read.big.matrix("2005.csv", type="integer", header=TRUE, backingfile="airline.bin", descriptorfile="airline.desc",

Dear helpers, Does anyone have information on the status of bigmemory and R3.0? Will it just take time for the devs to re-code for the new environment? Or is there an alternative for this new version? Thanks Ben Caldwell [[alternative HTML version deleted]]

Dear R-list I'm on my way to start a new project on a rather big panel, consisting of approximately 8 million observations in 30 waves of data and about 15 variables. I have a similar data set that is approximately 7 gigabytes in size. Until now I have done my data management in SAS, and Stata, mostly identifying spells, counting events in intervals, and a like, but I would like to

To answer your first question about read.big.matrix(), we don't know what your acc3.dat file is, but it doesn't appear to have been detected as a standard file (like a CSV file) or -- perhaps -- doesn't even exist (or doesn't exist in your current directory)? Next: > In addition, I am planning to do a multiple imputation with MICE package > using the data read by bigmemory

Dear all, bigKRLS, which has been on CRAN for a couple of years, had to be pulled recently due to what seems to be a sanitizer issue stemming from its use of bigmemory. bigKRLS works fine (we?ve used it ourselves on many different platforms and have had over 15,000 downloads without an end user reporting difficulties because of this issue). Unfortunately, we have been unable to reproduce the

I work with problems that have rather large data requirements -- typically a bunch of multigig arrays. Given how generous R is with using memory, the only way for me to work with R has been to use bigmatrices from bigmemory package. One thing that is missing a bit is interoperability of bigmatrices with C++ and possibly java. What i mean by that is API that would allow read and write filebacked

Hi, Is it possible for me to read data from shared memory created by a vc++ program into R using bigmemory? [[alternative HTML version deleted]]

My long-promised Rdsm package is now on CRAN. Some of you may recall that I made a prototype available on my own Web page last July. This is the official version, much evolved since I released the prototype. The CRAN description states: Provides a threads-like programming environment for R, usable both on a multicore machine and across a network of multiple machines. The package

I have pretty big data sizes, like matrices of .5 to 1.5GB so once i need to juggle several of them i am in need of disk cache. I am trying to use bigmemory package but getting problems that are hard to understand. I am getting seg faults and machine just hanging. I work by the way on Red Hat Linux, 64 bit R version 10. Simplest problem is just saving matrices. When i do something like

I recently learned about the bigmemory and foreach packages and am trying to use them to help me create a very large matrix. Without those packages, I can create the type of matrix that I want with 10 columns and 5e6 rows. I would like to be able to scale up to 5e9 rows, or more, if possible. I have created a simplified example of what I'm trying to do, below. The first part of the

Hi, With reference to the mail below, I have large datasets, coming from various different sources, which I can read into filebacked big.matrix using library bigmemory. I want to merge them all into one 'big.matrix' object. (Later, I want to run regression using library 'biglm'). I am unsuccessfully trying to do this from quite some time now. Can you please

Hi, I'm playing around with the 'bigmemory' package, and I have finally managed to create some really big matrices. However, only now I realize that there may not be functions made for what I want to do with the matrices... I would like to perform a cluster analysis based on a big.matrix. Googling around I have found indications that a certain kmeans.big.matrix() function should

At one point we might have gotten something working (older version?) on Solaris x86, but were never successful on Solaris sparc that I remember -- it isn't a platform we can test and support. We believe there are problems with BOOST library compatibilities. We'll try (again) to clear up the other warnings in the logs, though. !-) We should also revisit the possibility of a CRAN BOOST

Hi Folks, I've been bumping my head against the 4GB limit for 32-bit R. I can't go to 64-bit R due to package compatibility issues (ROBDC - possible but painful, xlsReadWrite - not possible, and others). I have a number of big dataframes whose columns all sorts of data types - factor, character, integer, etc. I run and save models that keep copies of the modeled data inside the model

Hi all, I'm on a Linux server with 48Gb RAM. I did the following: x <- big.matrix(nrow=20000,ncol=500000,type='short',init=0,dimnames=list(1:20000,1:500000)) #Gets around the 2^31 issue - yeah! in Unix, when I hit the "top" command, I see R is taking up about 18Gb RAM, even though the object x is 0 bytes in R. That's fine: that's how bigmemory is supposed to

Package "bigmemory" is now available on CRAN. A brief abstract follows: Multi-gigabyte data sets challenge and frustrate R users even on well-equipped hardware. C/C++ and Fortran programming can be helpful, but is cumbersome for interactive data analysis and lacks the flexibility and power of R's rich statistical programming environment. The new package bigmemory bridges this gap,

The re-engineered bigmemory package is now available (Version 3.5 and above) on CRAN. We strongly recommend you cease using the older versions at this point. bigmemory now offers completely platform-independent support for the big.matrix class in shared memory and, optionally, as filebacked matrices for larger-than-RAM applications. We're working on updating the package vignette, and a

The long-promised revision to bigmemory has arrived, with package 4.2.3 now on CRAN. The mutexes (locks) have been extracted and will be available through package synchronicity (on R-Forge, soon to appear on CRAN). Initial versions of packages biganalytics and bigtabulate are on CRAN, and new versions which resolve the warnings and have streamlined CRAN-friendly configurations will appear