https://github.com/kaneplusplus/bigmemory

https://github.com/kaneplusplus/bigmemory

Last synced: 9 months ago
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• Host: GitHub
• URL: https://github.com/kaneplusplus/bigmemory
• Owner: kaneplusplus
• Created: 2014-11-28T17:14:24.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2024-01-09T17:18:08.000Z (almost 2 years ago)
• Last Synced: 2025-03-29T06:07:34.837Z (9 months ago)
• Language: C++
• Size: 935 KB
• Stars: 127
• Watchers: 12
• Forks: 24
• Open Issues: 36
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• fucking-awesome-R - bigmemory - Shared memory and memory-mapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.r-project.org/web/packages/biglm/index.html)) and Random Forests (<b><code>&nbsp;&nbsp;&nbsp;&nbsp;91⭐</code></b> <b><code>&nbsp;&nbsp;&nbsp;&nbsp;23🍴</code></b> [bigrf](https://github.com/aloysius-lim/bigrf))). (Data Manipulation)
• jimsghstars - kaneplusplus/bigmemory - (C++)
• awesome-R - bigmemory - Shared memory and memory-mapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.r-project.org/web/packages/biglm/index.html)) and Random Forests ([bigrf](https://github.com/aloysius-lim/bigrf)). (Data Manipulation)
• awesome-R - bigmemory - Shared memory and memory-mapped matrices. The big\* packages provide additional tools including linear models ([biglm](http://cran.r-project.org/web/packages/biglm/index.html)) and Random Forests ([bigrf](https://github.com/aloysius-lim/bigrf)). (Data Manipulation)

README

          
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bigmemory

=========

Create, store, access, and manipulate massive matrices.  Matrices are, by default, allocated to shared memory and may use memory-mapped files. Packages ‘biganalytics’, ‘synchronicity’, ‘bigalgebra’, and ‘bigtabulate’ provide advanced functionality. Access to and manipulation of a ‘big.matrix’ object is exposed in by an S4 class whose interface is simlar to that of an ‘matrix’. Use of these packages in parallel environments can provide substantial speed and memory efficiencies.  ‘bigmemory’ also provides a C++ framework for the development of new tools that can work both with ‘big.matrix’ and native ‘matrix’ objects.

```{R}

x <- big.matrix(5, 2, type="integer", init=0,

                dimnames=list(NULL, c("alpha", "beta")))

x

x[1:2,]

x[,1] <- 1:5

x[,"alpha"]

colnames(x)

options(bigmemory.allow.dimnames=TRUE)

colnames(x) <- NULL

x[,]

```

### Wish List

1. Additional big.matrix subclasses (e.g. sparse, positive-definite, etc.) 

    -- see bigalgrebra package

2. big.sparse.matrix.