Parents (Total: 3)

The searched file hash is included in 3 parent files which include package known and seen by metalookup. A sample is included below:

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
FileSize	6129192
MD5	81E9E6410B1696520A3DC55E53F04561
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	Ubuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageName	r-cran-rsvd
PackageSection	gnu-r
PackageVersion	1.0.3-3build1
SHA-1	3C123CA67703B31289EA60824B340D90028FB933
SHA-256	B4A3047FBE29B99B5D4FDBA7F5D91D489663D4587FD39A3BA3C185D3026E7FF5

Key	Value
FileSize	6129076
MD5	D28875B7140E3F8475665FF674282DAA
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	1.0.3-3
SHA-1	E7774B8B9D1E844DC9B6E101BE59DC936CCBD750
SHA-256	8B0DA7C393BA4DA8D837C0B0DBB8CED0D942ADFBD2D1857E677B7F34DA58646E