Result for 63CE4F49E3B02203FA689722EC9DB58EE96F5BF6

Query result

Key Value
FileName./usr/lib64/R/library/brglm/help/brglm.rdx
FileSize428
MD58AC9AB8AD6E24B161FC2429B3172FB74
SHA-163CE4F49E3B02203FA689722EC9DB58EE96F5BF6
SHA-2565C8C585D937CE9D972E57BD913228068026C8E5202AF0E30AA6707FFA12471A4
SSDEEP6:XtRP262kkMW6NbMpy1GBEma5kp5Bq5jaZ6jJ4nXYOGTHVMPAveFxetIGpkQDVLkm:XHPeKMpGqEVk5BCjHJ4o5HttP/pAa
TLSHT164E023B281D1919C139C40D0B1C3F79088136D3654445B4B402D42241ED017314D9E6F
hashlookup:parent-total4
hashlookup:trust70

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Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5C5261CF61DC5F5DFC070A11AE84BC53E
PackageArchx86_64
PackageDescriptionFit 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.
PackageNameR-brglm
PackageReleaselp154.1.1
PackageVersion0.7.2
SHA-1EE999711DC4EA8E3C24BA525E5D2F7C7C91709AE
SHA-2564A886644D8DB971EA4BD966618C998085C414B5CBE0FCFE5A460698CD1F9841D
Key Value
MD57389C6FD4277CCE7A66A28074DE5A001
PackageArchx86_64
PackageDescriptionFit 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.
PackageNameR-brglm
PackageReleaselp152.1.4
PackageVersion0.7.2
SHA-1CC22F98B1C96CD6BB1024795C76184A812437EE8
SHA-25669049302FA2907C172D28E3B7AFB09F6C6886EBFB3EDB40834B9BB2A40F68EA0
Key Value
MD56CBEE56EED656AAE8AFE7C01F82ED182
PackageArchx86_64
PackageDescriptionFit 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.
PackageNameR-brglm
PackageRelease1.20
PackageVersion0.7.2
SHA-187507AB50873EB366A36D93741D108CAA18B3B0B
SHA-2564D7E0A86977773F572160D66A06A72C6FE924D91CA3FB66C88D2CEBA12584AA5
Key Value
MD55724C63933B2F8DE45D59D5A1B734426
PackageArchx86_64
PackageDescriptionFit 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.
PackageNameR-brglm
PackageReleaselp153.1.3
PackageVersion0.7.2
SHA-178F4A7164F96FDC7532CCA2BC3D71AEC35FD87FA
SHA-2566B666F3A0709954FD394C74BACAE349E6C35A0AAA773F9BEF0088F2C7257760D