Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

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

Key	Value
MD5	11EA55C7DD22300C06916BED7E921279
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	1.22
PackageVersion	1.0.5
SHA-1	A1889F1287A102B3422AA8DE49742159BC7D84AA
SHA-256	E0CCA0EC44CCC69133E7E6230BA28E1A3178F5F769E4F9E46391E5C34D98B4CB

Key	Value
MD5	ABB4E4599445FED1078DE264C316E917
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	lp153.1.2
PackageVersion	1.0.5
SHA-1	D9E2AEEFB1847BDA056DFFF282A3D1ED3C52D462
SHA-256	CDD711ED9330A5BB87487ACF18FBBE14A1925DBD1DB6D8E6CD8A6F64525DF665

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
MD5	EABDDF3D5E5B1CC65099F6BCF095A9F6
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	lp152.1.4
PackageVersion	1.0.5
SHA-1	DB929544BAE092390488EB323038564382704628
SHA-256	2A850161ED28F9172FDCE543D159AF7A83B5AB220BA39DF98D6D199FA336BE76