Title: PCA, Transpose and Multicore Functionality for 'big.matrix'
Depends: R (>= 3.0), grDevices, graphics, stats, utils, reader (>=
1.0.1), NCmisc (>= 1.1), bigmemory (>= 4.0.0), biganalytics
Imports: parallel, methods, bigmemory.sri, irlba
Description: Adds wrappers to add functionality for big.matrix objects (see the bigmemory project).
This allows fast scalable principle components analysis (PCA), or singular value decomposition (SVD).
There are also functions for transposing, using multicore 'apply' functionality, data importing
and for compact display of big.matrix objects. Most functions also work for standard matrices if

Title: PCA, transpose and multicore functionality for big.matrix
Imports: parallel, methods, bigmemory.sri, BH, irlba
Description: This package adds wrappers to add functionality for big.matrix objects
(see the bigmemory project). This allows fast scalable principle components analysis (PCA),
or singular value decomposition (SVD). There are also functions for transposing, using multicore
'apply' functionality, data importing and for compact display of big.matrix objects. Most functions
also work for standard matrices if RAM is sufficient.

Description: Read large text files by splitting them in smaller files.
Authors@R: person("Florian", "Privé", , "florian.prive.21@gmail.com", c("aut", "cre"))
Imports: data.table, Rcpp, parallel, fpeek

Package bigreadr also provides some convenient wrappers of fread/fwrite.
Imports: data.table, Rcpp, parallel, utils
Suggests: spelling, testthat, covr, RSQLite

Package 'bigreadr' also provides some convenient wrappers around fread()
Imports: bigassertr (>= 0.1.1), data.table, Rcpp, parallel, utils

Imports: data.table, Rcpp, parallel, fpeek, utils

Imports: bigassertr (>= 0.1.1), data.table, parallelly, Rcpp, utils

Title: Big Random Forests: Classification and Regression Forests for
Authors@R: c(person("Aloysius", "Lim", role = c("aut", "cre"),
Depends: R (>= 2.14), methods, bigmemory
Description: This is an implementation of Leo Breiman's and Adele Cutler's
Random Forest algorithms for classification and regression, with optimizations
for performance and for handling of data sets that are too large to be
processed in memory. Forests can be built in parallel at two levels. First,
trees can be grown in parallel on a single machine using foreach. Second,
multiple forests can be built in parallel on multiple machines, then merged
into one. For large data sets, disk-based big.matrix's may be used for storing
data and intermediate computations, to prevent excessive virtual memory
swapping by the operating system. Currently, only classification forests with
a subset of the functionality in Breiman and Cutler's original code are
implemented. More functionality and regression trees may be added in the