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	56AADE7A867F871B437C0FA66E4EA5CB
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	3.26
PackageVersion	0.5_9
SHA-1	B1D6D8B82AB45FB309F967FC9FB80DE82CE23447
SHA-256	5C80E2EBB1E917A441B857D1156C799E9953EB1AF326A8E0A20BFF0D08437C3E

Key	Value
MD5	29762D22F70AB427CB5D26F73C2D1952
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	3.15
PackageVersion	0.5_9
SHA-1	FADA6FCDBB0635E801A31B4A5870F6A2E5170FA0
SHA-256	B29D48AE22902E580179B3F312CC0FAE77DED8197F6D5A35692F85B289A3D39E

Key	Value
MD5	6707CD3E7C586268115756A90F1FBE29
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	3.16
PackageVersion	0.5_9
SHA-1	42EDCCFDDB4446674E896DE43511234FC2D5F9D5
SHA-256	F8299DEB9486F8BC08049CE3E0DB708A3D488171DA0251FDB3BC0EDA847A5827