Result for 197B1B7BAEE77B73C9D129D526F71FEDF6796D6F

Query result

Key Value
FileName./usr/lib64/R/library/rsvd/DESCRIPTION
FileSize1704
MD553CA6A2A1EBD5909F13DDFD6F429EBB1
SHA-1197B1B7BAEE77B73C9D129D526F71FEDF6796D6F
SHA-256E8C58EBBF3F475CAB9631467FFEA2688CA5D7380DDF546D05DD3845650FCF493
SSDEEP48:Hq7NfcN9CF4+4WJewhCsKlMbdwlyYs6Nzhy8aE:K76DCFBBewhCsdwlq6ljaE
TLSHT1C73174423B88238C078D72D6BFE58B91D67CF61827B6948878170638037F92657A779D
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5A28601C83F9C8D428D00AF72CBAC0879
PackageArchx86_64
PackageDescriptionLow-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>.
PackageNameR-rsvd
PackageRelease1.15
PackageVersion1.0.2
SHA-1106E10AC8A4B9B1FD639FA8965381BD11066BC29
SHA-2562F25B5EAC65AC870582208A6A87DB8375D70FB9E37F4DC434E225FA1A7DAC09B