irlba: Fast Truncated SVD, PCA and Symmetric Eigendecomposition for Large Dense and Sparse Matrices

Fast and memory efficient methods for truncated singular and eigenvalue decompositions and principal component analysis of large sparse or dense matrices.

AuthorJim Baglama [aut, cph], Lothar Reichel [aut, cph], B. W. Lewis [aut, cre, cph]
Date of publication2016-09-21 19:27:40
MaintainerB. W. Lewis <blewis@illposed.net>
LicenseGPL-3
Version2.1.2

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Files in this package

irlba
irlba/inst
irlba/inst/doc
irlba/inst/doc/irlba.pdf
irlba/inst/doc/irlba.Rnw
irlba/tests
irlba/tests/edge.R
irlba/tests/test.R
irlba/src
irlba/src/Makevars
irlba/src/irlb.h
irlba/src/utility.c
irlba/src/irlb.c
irlba/NAMESPACE
irlba/demo
irlba/demo/00Index
irlba/demo/custom_matrix_multiply.R
irlba/R
irlba/R/irlba.R irlba/R/utility.R irlba/R/prcomp.R irlba/R/eigen.R
irlba/vignettes
irlba/vignettes/irlba.Rnw
irlba/README.md
irlba/MD5
irlba/build
irlba/build/vignette.rds
irlba/DESCRIPTION
irlba/man
irlba/man/prcomp_irlba.Rd irlba/man/irlba.Rd irlba/man/partial_eigen.Rd

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