Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

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	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