Parents (Total: 13)

The searched file hash is included in 13 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	79AFB410EA5EDB9829D7B744FF4C5F5D
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	A60193D637C8302B72442962DC5EF7FFFFC504AC
SHA-256	50176760D5FE319E279AE867C13CD93C76C0D5E7D5ADBC24ABCB3C44E89359FE

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
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
MD5	6CC28354FF2696450C24ABCEBC8FB2EC
PackageArch	armv7hl
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	1.29
PackageVersion	1.0.2
SHA-1	C0718CBDE0CA92C60546C6ECEBA358F3D24191CE
SHA-256	032C8436867984568EF263E9BB5D7D0EF226AD1F331A8BAC9256C11BEFF31CF8

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	7D7B506A84E9F2006BA23186C1968E6D
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	A0ED05B93392E5A75273E147ED154A000437DA3B
SHA-256	2080823EE44589AE3083EE20AC8579C8BFE6270C9229150727C1A27761B00B34

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

Key	Value
MD5	15D778B1645D3AD1BFE63EE830FBEFC7
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	DA8AB18DF498FB81EFAA1729BFC69895A3CA73D0
SHA-256	E7015748930CD054C0AED30717AA3CCCA637F045D8B1185E10AD1999DFC648D9

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	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	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