Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	5855B80163A3AAEBF786F12483D239E9
PackageArch	x86_64
PackageDescription	Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.
PackageName	R-msm
PackageRelease	2.27
PackageVersion	1.4
SHA-1	F92A4AA28A6E774ABC7BFAFB2E760A72309D23CC
SHA-256	118135D31BFBC1C69957E664A5EA627677F8094C4216559C95AF72C95E357BA5

Key	Value
MD5	4FE3515A2156FF32E8EE00382D583BC7
PackageArch	x86_64
PackageDescription	Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.
PackageName	R-msm
PackageRelease	2.15
PackageVersion	1.4
SHA-1	354D89F443AFE6543BCB215821E6E2210F3BFCB4
SHA-256	CFB0B2A5BFCFDE134F6AF026C97A56275CB6F5F5F6FC6A72FDD7450281BE6E7E

Key	Value
MD5	DC344B9BFF03DD88A43D29DC3926F4C3
PackageArch	x86_64
PackageDescription	Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.
PackageName	R-msm
PackageRelease	2.236
PackageVersion	1.4
SHA-1	2A8EBA10C4966E1A4EA137BC9E03E1926AA048E3
SHA-256	26FAF7C70A2F4463E15210F750369A43F048260037C901913979C1D3696E2B82

Key	Value
MD5	39B6419F110985D4DE9880D6077937C0
PackageArch	x86_64
PackageDescription	Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.
PackageName	R-msm
PackageRelease	2.236
PackageVersion	1.4
SHA-1	28B5EB8288A50F225E5492A6BF1100FCCECD2C22
SHA-256	D278561D8FDF12F5CDD28CDEC5F16FCEE190D677F265EF30494AA29C4078F569

Key	Value
MD5	7556CB025FC19F20C01C6B9B7F9B6B43
PackageArch	x86_64
PackageDescription	Functions for fitting general continuous-time Markov and hidden Markov multi-state models to longitudinal data. A variety of observation schemes are supported, including processes observed at arbitrary times (panel data), continuously-observed processes, and censored states. 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.
PackageName	R-msm
PackageRelease	2.16
PackageVersion	1.4
SHA-1	1B40916FDBCD510DAFC008DE933C6E6D1F4AE2E6
SHA-256	AEA85EC2DB22DD4D2A0B526D8E9C8443B1B7720B3A2650E506D00893E46CAC0E