Parents (Total: 17)

The searched file hash is included in 17 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	B9E647E17D49571CB4D7C063518FD7A1
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	lp153.3.12
PackageVersion	0.5_9
SHA-1	15E34DD893E734654A49196439D74F8E852E75EF
SHA-256	DA005EB073906E220BA813B5567FD426DC2B128FAFDBDB62E999559C0C32849D

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

Key	Value
MD5	05CCCA38069C76223CDC895DEFB13365
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	lp150.3.48
PackageVersion	0.5_9
SHA-1	507D00E665869828A39E7157C279194FF8FB1918
SHA-256	0EE733EAF24F3DB28775BADB6FC9BA4AD4CB066AB71F0DD2BAF01389F2F4E350

Key	Value
MD5	912002974319CA69CD812B7B882E4087
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	3.228
PackageVersion	0.5_9
SHA-1	6544396DD879F27EBC274943172A9C6252F4698A
SHA-256	3C8D1710532B0FAA04CE28ACD6E53B93B543B238AA1DA0CF46314209AE8B3AF9

Key	Value
MD5	5724C63933B2F8DE45D59D5A1B734426
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	lp153.1.3
PackageVersion	0.7.2
SHA-1	78F4A7164F96FDC7532CCA2BC3D71AEC35FD87FA
SHA-256	6B666F3A0709954FD394C74BACAE349E6C35A0AAA773F9BEF0088F2C7257760D

Key	Value
MD5	5973830AEB84AF8516D71EBA12EAD45B
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	lp152.3.12
PackageVersion	0.5_9
SHA-1	86CB437A85653021C4DC9EABCC7ADF334FEB74A8
SHA-256	24F041A5384B9CD9D2F4842E686B90E2917AF384BBD91685E737D216CC5CF119

Key	Value
MD5	6CBEE56EED656AAE8AFE7C01F82ED182
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	1.20
PackageVersion	0.7.2
SHA-1	87507AB50873EB366A36D93741D108CAA18B3B0B
SHA-256	4D7E0A86977773F572160D66A06A72C6FE924D91CA3FB66C88D2CEBA12584AA5

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	F6A80F98A40868424E95AD850A01F8FB
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	3.229
PackageVersion	0.5_9
SHA-1	983CDDCA7DB9481B919D9789FE3BC21BB03A9056
SHA-256	6AF8368EA2D55999C6F10BBDD566BDE729718CCB40E92C6EB69F22DC8F308477

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