Parents (Total: 13)

The searched file hash is included in 13 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	922F46BBD6F3D118FD467C87939598C4
PackageArch	i586
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	04E6FF270AD98AC70572B0E0B6D007113AE2F5D6
SHA-256	0D4B96093B3E91B4E16EF02D404F5901E4D80C2414923ECCB5FFDE084853A304

Key	Value
MD5	1D1342AA7C56F1294B42CAD92A74CB5C
PackageArch	i586
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	85C04B614CA326DA7982F7778C88B3FB426CBA28
SHA-256	048F09E1927D7D5CE9569A672BE7DB86B0BF4658BD055C2736735BD367B088BC

Key	Value
MD5	610BE6930C0310A4E2C482E5537E406C
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	lp153.2.12
PackageVersion	1.4
SHA-1	CFB4013CEDDF349A883F11AF6F458EB83DE86F48
SHA-256	F0CB01AC0C4784468FCAB4604F0E59A89F77B2706DE551588A37DF57ED8F15FA

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	A5F828AE824CE81BA084A7809D7B92A9
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	lp152.2.14
PackageVersion	1.4
SHA-1	99B1DD82FAA7EEA16FC27F12F68108EB332A71C7
SHA-256	0F0875873D12E00FDAC94B6098635073A8D86FD54C0BCEAABD1AD60DF756A063

Key	Value
MD5	8DC8D7500D7E052CB058AF3DB8FB3A34
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	lp150.2.49
PackageVersion	1.4
SHA-1	7A26E16426B8140F7D81D3503D6DEE2EA3801B8B
SHA-256	2E51325C06FEF5989080C84BA2687068C910308D3C7B690542F837310FD7FE12

Key	Value
MD5	086A694F7CB1070926217A88D814D753
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	2.6
PackageVersion	1.4
SHA-1	CE2F1B17A0B60C31013172CCA46132FECED88544
SHA-256	B5AAF6004A82B277EC70C6C05CF8EF8F1FB0D918337645CC689272DD2B6A2990

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

Key	Value
MD5	B8991BA42431BBAEF482F5E6864B0F74
PackageArch	armv7hl
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	2.138
PackageVersion	1.4
SHA-1	7E441A3388A2FA3CCABC58CEE3F9B7F95D57C2ED
SHA-256	569A1963907386B349B8E57A96354B011716532F6F141D966D6EC76673C37E98

Key	Value
MD5	9144E198D71718D63335CBEBAB05D857
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	lp151.2.59
PackageVersion	1.4
SHA-1	0500501E30F2021928DC92EFDAC7813A3E18389C
SHA-256	25297FE0EA198BA15C874A6049803B93F54EEA023A40D50238A6745730364A63