Result for 3B046AD241DA4EE0F5B9E0250613277F6D2DCB06

Query result

Key Value
FileName./usr/lib64/R/library/Numero/DESCRIPTION
FileSize1350
MD57137900C0E5472DAF3114CDE7A704672
SHA-13B046AD241DA4EE0F5B9E0250613277F6D2DCB06
SHA-256E3D549D51C6625D609879B74E6BDD0B27E60DE0E315C8D219BA4EB017B4CB2D0
SSDEEP24:ONHoLnbKHGMJb8MUVeHfBUGf+ZOHVKbxoonVS2Isw6wEeI3aN+mFWLbiwdF0Uopr:52HGmBuGjDGlw6wW3H5gUCUaIUd
TLSHT1B221B681E7D8D12649E341DA3621A3FAD7F5D51E33E24174480C82783212C452BEB3E1
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
MD54232496C2C2A6B32B2234BF9C5ECACA5
PackageArchx86_64
PackageDescriptionHigh-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
PackageNameR-Numero
PackageReleaselp153.1.2
PackageVersion1.8.4
SHA-1FED4080936CE403FE2E0E45E50FC048E794014D6
SHA-256F01D4BC3C031797ED1B4CD03F8E0E72BDA8AEEB97217956C87327B7D253621F6