Result for 059AA10E7166EE15858223703E9D3FD15F909BCA

Query result

Key Value
FileName./usr/lib64/R/library/Numero/Meta/hsearch.rds
FileSize847
MD55CA094C8523DAEEBB999F90C639A4B29
SHA-1059AA10E7166EE15858223703E9D3FD15F909BCA
SHA-2565732A1029940419247A8EE7FCB1EE498BA442D72BF69079B84655F2428BB457B
SSDEEP12:XFfWK49G+54+PTr55Mkbbhybe8fyYMisgmmutBeYF6a7sQSyAwwdXTp4lQHfaG4:XFW4+LckCeeyYMoVCgktSPwYXK6ff4
TLSHT1A301DA129CB4A5E5787A2D57CBCD11799F1354EE45E03D10C243C4EE6419BC0C5ED553
hashlookup:parent-total4
hashlookup:trust70

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Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5720454A6901588F59027A818CB7B6EBB
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
PackageRelease1.9
PackageVersion1.8.4
SHA-1917099E68EC03F4DA6CA7D1E283B3C515B429058
SHA-256C7A8ACC52EEFED15D640C11048F748B8CC89FEC4BD36BECAF471C4F91CED1BA3
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
Key Value
MD534E954324378B2C3F57CEBD3B2F829D4
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
PackageReleaselp152.1.2
PackageVersion1.8.4
SHA-196BE57AE4E1C8047578E8B593441CC4B2CE846C4
SHA-2569A511B3B4AF02A143C97AE783AD0A5FD1ADEFD166BFB2FADC04BD5AFEDAC0268
Key Value
MD5392586DC3B1A58B293C010FF501C3EAE
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
PackageReleaselp154.1.1
PackageVersion1.8.4
SHA-128FE30A9728D0768840AD04BB416EE36373D1926
SHA-256198C5C938E1E600E5B923DEFA204317DD33177E93E243DC07C38A1CDB72EC5BF