Result for 0D5144D33E63C844F4EBDA6870E7E7A9F1EBD71D

Query result

Key Value
FileName./usr/lib64/R/library/msm/Meta/data.rds
FileSize268
MD56B47C13331A1776B543C5966267BBFD7
SHA-10D5144D33E63C844F4EBDA6870E7E7A9F1EBD71D
SHA-2564F77D7CA83EE8EB82928EE78A2D781FABD4623F14A299914B7F28C91F482376C
SSDEEP6:XtBxZ8SmCBqWnmkIJDTWGscuVdavj5Z45raTwOUU:XzjfBZmtNTWGscT5ZIra5
TLSHT112D095483084F5DBD234EC34AF37CBB4BC079E444076C8D470E1DF41655C696966111D
hashlookup:parent-total3
hashlookup:trust65

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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
MD54C9354E9DFD1B93A40CA1911BF5E6C72
PackageArcharmv7hl
PackageDescriptionFunctions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modeled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.
PackageMaintainerFedora Project
PackageNameR-msm
PackageRelease6.fc33
PackageVersion1.6.8
SHA-1607EA9CE8A1821786FE5B97F904D2E45E9A484AA
SHA-256015F5C19DE889472FC18335A8741964093A0A413B232CC4A7115EE632A21A645
Key Value
MD5C60717E27290397E60CDE176AE05D1AF
PackageArchaarch64
PackageDescriptionFunctions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modeled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.
PackageMaintainerFedora Project
PackageNameR-msm
PackageRelease6.fc33
PackageVersion1.6.8
SHA-13FD703FABCF2C208E1B2808B2CCB4497999A1F67
SHA-2563A710932062EC546DAB551E9856F4043F6409176EF5B9AE72C2FF4F9D12B997A
Key Value
MD5F35201A9EC1CB169413B6A68B5612206
PackageArchx86_64
PackageDescriptionFunctions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. Both Markov transition rates and the hidden Markov output process can be modeled in terms of covariates. A variety of observation schemes are supported, including processes observed at arbitrary times, completely-observed processes, and censored states.
PackageMaintainerFedora Project
PackageNameR-msm
PackageRelease6.fc33
PackageVersion1.6.8
SHA-1E1886E23185C53604258DB31111FA27D52A3D1FA
SHA-256DAA6495B148847E4013076FB4F0FB687BC60F8393FD8BF4519991ADA7752922F