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	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	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	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	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	8E11A12C9ED3B17C18E13C7FA3A862A5
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	lp151.3.60
PackageVersion	0.5_9
SHA-1	B36FB1D03DC50CA991FD377B3E9E8EFFF7A93958
SHA-256	758D0051AA5E962ED74DD5D84CD3CBE4F991455F0573B7BAE5FA18AFA4A5412F

Key	Value
MD5	DADAB67A63468319DE624AC1F9E9270C
PackageArch	armv7hl
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	3.181
PackageVersion	0.5_9
SHA-1	E1418144096C01506951904A8E9D9C6C63F16D05
SHA-256	093D7FC9E2E0E8C3849BB70087382299EDCCC03D91CE43789CEE8A458080E895

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	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	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

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	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	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	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