Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	3C32515200B0060BB0046731FDC784BF
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	lp150.1.4
PackageVersion	1.0.2
SHA-1	E2B64F2DBDF666C894F1678C402F75C71DC09E9D
SHA-256	A05F4267D7DBACC35B2E805CB4D4B9C4EAA752974BB0C017D4BDACD9FF6766AC

Key	Value
MD5	37358972E330270BA98C64072263EC26
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	lp151.1.9
PackageVersion	1.0.2
SHA-1	E3687E13D4947BA7DB6122D0C3158F4506E3A084
SHA-256	54A005564A62AA06B91EF88F4CAFEFD1DD3E28D7B4BC8A4EE6E58B2A679EF807

Key	Value
MD5	C09137DF59C9E0A01116C48425616E19
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	lp153.1.9
PackageVersion	1.0.2
SHA-1	C3B20936D9E7954F88AAF70E85D76EF44023DD5B
SHA-256	AD05F9A8AAD4FE746B476A6ED017B7D71A60A32D45200B5A6CF74BED49AA35CE

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