Result for 4C8045F43A5421437266B810639A802C4CF79E7D

Query result

Key Value
FileName./usr/lib64/R/library/msm/Meta/links.rds
FileSize1149
MD5220E5CD08753232E79434AF2B412B14A
SHA-14C8045F43A5421437266B810639A802C4CF79E7D
SHA-25681F7E714F1D8C2F0175CDC8F62D40C780F7FC2CEB0624857D0240DCCC5A5372A
SSDEEP24:XWXLItd7bYprewNrp5RGtUHUtzmFIQVm7XjrsOFY:XWO0r9Nrp5RGtuUwFM7T4O+
TLSHT15B21B9CFCCD06CEFAB828EB59D8BD7475A40AFA2C9DADD11350714D91089D4ED6E0402
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