Result for 2D61CF18C720327305BF29A771C670B67610EF0B

Query result

Key Value
FileName./usr/lib64/R/library/msm/help/msm.rdb
FileSize329502
MD538E2C41A42CE088AF1A92F1C0AD06706
SHA-12D61CF18C720327305BF29A771C670B67610EF0B
SHA-25617E0A9561774E920B041FD04546E76E34AD6FA92C67FAE0133458B0E8AD48400
SSDEEP6144:V8Op62dCwg2K2ZYHRCpa0MsxElpvIpT+NybUnHDeP23C4pQMiEdU+PA:VH3TZY+aJDTvIpS8UnHDx3C9/GPA
TLSHT1D26423F4B30529A9C66B68C0549FF005FE9BC80C563C5493B6BC51BB56F76EB08A702A
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
MD5B49EA7BCA3EB56DBA2A7B06F0B439D89
PackageArchx86_64
PackageDescriptionFunctions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.
PackageNameR-msm
PackageRelease1.7
PackageVersion1.6.9
SHA-1D7592E93984D7A467A960D5B56F8EC758897F2FC
SHA-256E4A2EAA4ADDD5D0D1C9C8D33A5794E67254DED0ECC0F477949756AC7C86C81C4
Key Value
MD5225CB7695CCC006F0A6E98A6F8219221
PackageArchx86_64
PackageDescriptionFunctions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.
PackageNameR-msm
PackageReleaselp153.1.2
PackageVersion1.6.9
SHA-132581E3764DEBC5F1F771AADBDF7E4F84A3E1869
SHA-25642EA47808C1C7DE33DEF8ED06A3DDAD5AA2413911C15E6DB660EC77705455617
Key Value
MD5080B633AF144E21E8E7B25AD022CDD33
PackageArchx86_64
PackageDescriptionFunctions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.
PackageNameR-msm
PackageReleaselp152.1.2
PackageVersion1.6.9
SHA-1AB259BCD2522F085EC625A5EE13FFA7C35137119
SHA-2561C02EA4250C70D3024AC92DFA681071C81EA4D754A3E2357439444E6F66D826C
Key Value
MD5E31A54DEF8E644DF4A730D022274E04E
PackageArchx86_64
PackageDescriptionFunctions for fitting continuous-time Markov and hidden Markov multi-state models to longitudinal data. Designed for processes observed at arbitrary times in continuous time (panel data) but some other observation schemes are supported. Both Markov transition rates and the hidden Markov output process can be modelled in terms of covariates, which may be constant or piecewise-constant in time.
PackageNameR-msm
PackageReleaselp154.1.1
PackageVersion1.6.9
SHA-1D588D246749D538ED1BA9AA415B5DE2D5676B5A1
SHA-256298411E9D4682D215A00C3E96206EA61E986A1920AA78DBC430CBE389FC0B274