Encoding: UTF-8
Package: bigstatsr
Type: Package
Title: Statistical Tools for Filebacked Big Matrices
Version: 1.2.3
Date: 2020-03-11
Authors@R: c(
  person("Florian", "Privé", 
         email = "florian.prive.21@gmail.com", 
         role = c("aut", "cre")),
  person("Michael", "Blum", 
         email = "michael.blum@univ-grenoble-alpes.fr", 
         role = "ths"),
  person("Hugues", "Aschard", 
         email = "hugues.aschard@pasteur.fr",
         role = "ths"))
Description: Easy-to-use, efficient, flexible and scalable statistical tools.
  Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping.
  It provides for instance matrix operations, Principal Component Analysis,
  sparse linear supervised models, utility functions and more
  <doi:10.1093/bioinformatics/bty185>.
License: GPL-3
Language: en-US
LazyData: TRUE
ByteCompile: TRUE
Depends: R (>= 3.3)
Imports: bigassertr (>= 0.1.1), bigparallelr (>= 0.2.3), cowplot,
        foreach, ggplot2 (>= 3.0), graphics, methods, Rcpp, RSpectra,
        stats, tibble, utils
LinkingTo: Rcpp, RcppArmadillo, rmio (>= 0.1.3)
Suggests: biglasso, bigmemory (>= 4.5.33), bigreadr (>= 0.2), covr,
        data.table, glmnet, hexbin, memuse, ModelMetrics, RhpcBLASctl,
        spelling (>= 1.2), testthat
RoxygenNote: 7.0.2
URL: https://privefl.github.io/bigstatsr
BugReports: https://github.com/privefl/bigstatsr/issues
Collate: 'AUC.R' 'FBM-attach.R' 'crochet.R' 'FBM.R' 'FBM-code256.R'
        'FBM-copy.R' 'RcppExports.R' 'SVD.R' 'apply-parallelize.R'
        'biglasso.R' 'bigstatsr-package.R' 'colstats.R'
        'crossprodSelf.R' 'mult-mat.R' 'mult-vec.R' 'plot.R'
        'predict.R' 'randomSVD.R' 'read-write.R' 'scaling.R'
        'summary.R' 'tcrossprodSelf.R' 'transpose.R' 'univLinReg.R'
        'univLogReg.R' 'utils-assert.R' 'utils.R' 'zzz.R'
NeedsCompilation: yes
Packaged: 2020-03-11 21:00:11 UTC; au639593
Author: Florian Privé [aut, cre],
  Michael Blum [ths],
  Hugues Aschard [ths]
Maintainer: Florian Privé <florian.prive.21@gmail.com>
Repository: CRAN
Date/Publication: 2020-03-12 13:20:05 UTC
Built: R 4.0.0; x86_64-w64-mingw32; 2020-04-10 11:21:55 UTC; windows
Archs: i386, x64
