Result for 25D85B6169E4112B101B4ED684FF9AD7055342D9

Query result

Key Value
FileName./usr/lib64/R/library/brglm/R/brglm.rdx
FileSize590
MD5B0CE5B47C26BD84D51875D3F532A5220
SHA-125D85B6169E4112B101B4ED684FF9AD7055342D9
SHA-2564E77A11FA7B3F821062613E11CE9C7AACCE24E374DFCED24EC224977A428742F
SSDEEP12:Xvwit59DHTRHlbVL+WjanI4XfE4QuLeWYw/ppm7Gzfl:XvwitHlL5efc8qPwRCGzN
TLSHT1B0F0B713A83E4CC856DA51E20580C91B2C67C5DD0007F82CC3ED94312F04D52E610141
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

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
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