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	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	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