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 an S4
class whose interface is similar to that of a `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.

For obvious reasons memory that the `big.matrix`

uses is managed outside
the R memory pool available to the garbage collector and the memory occupied
by the `big.matrix`

is not visible to the R.
This has subtle implications:

Memory usage is not visible via general R functions (e.g. the

`gc()`

function)Garbage collector is mislead by the very small memory footprint of the

`big.matrix`

object (which acts merely as a pointer to the external memory structure), which can result in much less eagerness to garbage-collect the unused`big.memory`

objects. After removing a last reference to a big`big.matrix`

, user should manually run`gc()`

to reclaim the memory.Attaching the description of already finalized

`big.matrix`

and accessing this object will result in undefined behavior, which simply means it will crash the current R session with no hope of saving the data in it. To prevent R from de-allocating (finalizing) the matrices, user should keep at least one`big.memory`

object somewhere in R memory in at least one R session on the current machine.Abruptly closed R (using e.g. task manager) will not have a chance to finalize the

`big.matrix`

objects, which will result in a memory leak, as the`big.matrices`

will remain in the memory (perhaps under obfuscated names) with no easy way to reconnect R to them.

Index of functions/methods (grouped in a friendly way):

big.matrix, filebacked.big.matrix, as.big.matrixis.big.matrix, is.separated, is.filebacked

describe, attach.big.matrix, attach.resource

sub.big.matrix, is.sub.big.matrix

dim, dimnames, nrow, ncol, print, head, tail, typeof, length

read.big.matrix, write.big.matrix

mwhich

morder, mpermute

deepcopy

flush

Multi-gigabyte data sets challenge and frustrate users, even on well-equipped hardware. Use of C/C++ can provide efficiencies, but is cumbersome for interactive data analysis and lacks the flexibility and power of 's rich statistical programming environment. The package bigmemory and associated packages biganalytics, synchronicity, bigtabulate, and bigalgebra bridge this gap, implementing massive matrices and supporting their manipulation and exploration. The data structures may be allocated to shared memory, allowing separate processes on the same computer to share access to a single copy of the data set. The data structures may also be file-backed, allowing users to easily manage and analyze data sets larger than available RAM and share them across nodes of a cluster. These features of the Bigmemory Project open the door for powerful and memory-efficient parallel analyses and data mining of massive data sets.

This project (bigmemory and its sister packages) is still actively developed, although the design and current features can be viewed as "stable." Please feel free to email us with any questions: bigmemoryauthors@gmail.com.

For example, `big.matrix`

, `mwhich`

,
`read.big.matrix`

```
# NOT RUN {
# Our examples are all trivial in size, rather than burning huge amounts
# of memory.
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[,]
# }
```

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