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
MD5	720454A6901588F59027A818CB7B6EBB
PackageArch	x86_64
PackageDescription	High-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.
PackageName	R-Numero
PackageRelease	1.9
PackageVersion	1.8.4
SHA-1	917099E68EC03F4DA6CA7D1E283B3C515B429058
SHA-256	C7A8ACC52EEFED15D640C11048F748B8CC89FEC4BD36BECAF471C4F91CED1BA3

Key	Value
MD5	4232496C2C2A6B32B2234BF9C5ECACA5
PackageArch	x86_64
PackageDescription	High-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.
PackageName	R-Numero
PackageRelease	lp153.1.2
PackageVersion	1.8.4
SHA-1	FED4080936CE403FE2E0E45E50FC048E794014D6
SHA-256	F01D4BC3C031797ED1B4CD03F8E0E72BDA8AEEB97217956C87327B7D253621F6

Key	Value
MD5	34E954324378B2C3F57CEBD3B2F829D4
PackageArch	x86_64
PackageDescription	High-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.
PackageName	R-Numero
PackageRelease	lp152.1.2
PackageVersion	1.8.4
SHA-1	96BE57AE4E1C8047578E8B593441CC4B2CE846C4
SHA-256	9A511B3B4AF02A143C97AE783AD0A5FD1ADEFD166BFB2FADC04BD5AFEDAC0268

Key	Value
MD5	392586DC3B1A58B293C010FF501C3EAE
PackageArch	x86_64
PackageDescription	High-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.
PackageName	R-Numero
PackageRelease	lp154.1.1
PackageVersion	1.8.4
SHA-1	28FE30A9728D0768840AD04BB416EE36373D1926
SHA-256	198C5C938E1E600E5B923DEFA204317DD33177E93E243DC07C38A1CDB72EC5BF