Parents (Total: 21)

The searched file hash is included in 21 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	B8C76184E2C0C57C16D2766EFF967CA6
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	1.14
PackageVersion	1.0.2
SHA-1	014E0350840FC7D79AA90593DA844DBCB4C68DFD
SHA-256	0F4C80C6A304F41AE4DB1F5D5508F007ABA85582DCCF192B8E5FDD532F38BF93

Key	Value
MD5	8664F5137631194C021832E8DB338F5F
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	lp152.1.8
PackageVersion	1.0.2
SHA-1	028DD563BAFAFF791F91374E86F3FCBE7A442C04
SHA-256	A7E6D8C8A704260EB7C167761F6A81971705EAA855544ED3AA06FE7B5D98BF3E

Key	Value
MD5	A28601C83F9C8D428D00AF72CBAC0879
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	1.15
PackageVersion	1.0.2
SHA-1	106E10AC8A4B9B1FD639FA8965381BD11066BC29
SHA-256	2F25B5EAC65AC870582208A6A87DB8375D70FB9E37F4DC434E225FA1A7DAC09B

Key	Value
MD5	E0D5A712EB96EFCBF5B303550D9AD92B
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	1.33
PackageVersion	1.0.2
SHA-1	1474C87A273BE47802A964CE010895B37BB8405B
SHA-256	948CD21C97AD7D760F405EB4ECC73B278DCBC59B596EED24A1C96C0ABBAD5138

Key	Value
MD5	91D00DB8648B943A7130330C426EFD65
PackageArch	i586
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	1.33
PackageVersion	1.0.2
SHA-1	2B78B2EF578A2312993D876A6D19541A0934F8CA
SHA-256	72AE324915BD97250103CC6871AAE4719DAFCFDC37B2A3DCCA36B850F5FD4FA8

Key	Value
FileSize	3588980
MD5	23C0BF9B14DC3D802FCF46E920CA7820
PackageDescription	Randomized Singular Value Decomposition Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageMaintainer	Debian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageName	r-cran-rsvd
PackageSection	gnu-r
PackageVersion	1.0.5-1
SHA-1	36B7D4C23852BFA47C959A59E11FDE049C2D6B52
SHA-256	117C9626FAD0BF928D81E96DAB144B3F09F97BB3A0566A9AF60E423C15417DEC

Key	Value
FileSize	6129192
MD5	81E9E6410B1696520A3DC55E53F04561
PackageDescription	Randomized Singular Value Decomposition Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-rsvd
PackageSection	gnu-r
PackageVersion	1.0.3-3build1
SHA-1	3C123CA67703B31289EA60824B340D90028FB933
SHA-256	B4A3047FBE29B99B5D4FDBA7F5D91D489663D4587FD39A3BA3C185D3026E7FF5

Key	Value
MD5	6C8A693616842063EA4AC1426AF72395
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageName	R-rsvd
PackageRelease	1.6
PackageVersion	1.0.2
SHA-1	403F754860A6ACFAB6AB643E63FE50418B8EFE0A
SHA-256	E1A1A0A542B018242B047993C30027B902D38123F93E6086D4D601906B7E3AE7

Key	Value
FileSize	6131088
MD5	76BF9E8DAE259B35328DD3A51D5E334A
PackageDescription	Randomized Singular Value Decomposition Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
PackageMaintainer	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-rsvd
PackageSection	gnu-r
PackageVersion	1.0.3-1
SHA-1	4705D37330F1C9EAAD6384493A48B4B024BE40A5
SHA-256	7D1F4770752BD9514DEDFC3A474C6BE46B4DDB68356343DD509D0B0A6CB0D468

Key	Value
MD5	5D97F9D8579509D487A36D5159205AEC
PackageArch	x86_64
PackageDescription	Low-rank matrix decompositions are fundamental tools and widely used for data analysis, dimension reduction, and data compression. Classically, highly accurate deterministic matrix algorithms are used for this task. However, the emergence of large-scale data has severely challenged our computational ability to analyze big data. The concept of randomness has been demonstrated as an effective strategy to quickly produce approximate answers to familiar problems such as the singular value decomposition (SVD). The rsvd package provides several randomized matrix algorithms such as the randomized singular value decomposition (rsvd), randomized principal component analysis (rpca), randomized robust principal component analysis (rrpca), randomized interpolative decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot functions are provided.
PackageName	R-rsvd
PackageRelease	lp154.1.1
PackageVersion	1.0.5
SHA-1	678E3D5B75D3EEC4CB0BF760C3307DE07F437CE8
SHA-256	AFF0D7E0F701CB238A4997C57504A2317987ABD6A12FAFB15B2AF7BD43CC9E76