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
MD5	EE4F685A8202ECB56C55196782CC5AC5
PackageArch	i586
PackageDescription	Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
PackageName	R-brglm
PackageRelease	3.229
PackageVersion	0.5_9
SHA-1	89B5D80B93BEFD77A7F87E099B90450B70D49602
SHA-256	E9FBDA0A20252EB609133C6F3F07F649D6EE59561106443767E2E151E2ED0BF8

Key	Value
MD5	D4C0DC4BA189451015B0104B2D977607
PackageArch	x86_64
PackageDescription	Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
PackageName	R-brglm
PackageRelease	3.6
PackageVersion	0.5_9
SHA-1	C6F95E0AAD3BF15B5ED57CA79B53BE611EDDA51F
SHA-256	0F357B9BFF78AF0759482FB6C494DB27D4570B648920042F4403F9F674377640

Key	Value
MD5	2AC6AB4CF56E9F4F2E31DC5125E7A776
PackageArch	i586
PackageDescription	Fit generalized linear models with binomial responses using either an adjusted-score approach to bias reduction or maximum penalized likelihood where penalization is by Jeffreys invariant prior. These procedures return estimates with improved frequentist properties (bias, mean squared error) that are always finite even in cases where the maximum likelihood estimates are infinite (data separation). Fitting takes place by fitting generalized linear models on iteratively updated pseudo-data. The interface is essentially the same as 'glm'. More flexibility is provided by the fact that custom pseudo-data representations can be specified and used for model fitting. Functions are provided for the construction of confidence intervals for the reduced-bias estimates.
PackageName	R-brglm
PackageRelease	3.228
PackageVersion	0.5_9
SHA-1	B6D4D4EF06DD7E4915D317EF918D62EDD321DA93
SHA-256	F0F3DAB40DAE5B5E108D4A1A9825EC005D609B58FB841D8237389EAFBE3C33F2