Result for 88B6593669C9E0EF814B8B49831CDA112F0B094C

Query result

Key Value
FileName./usr/lib/R/library/brglm/libs/brglm.so
FileSize14252
MD5E2AD60D1A89DDF82A30F458E355A6B15
SHA-188B6593669C9E0EF814B8B49831CDA112F0B094C
SHA-2563628854F032A36F9DBE42F7A76C06806AF6CCBA096AEC71CEEB52F002AA27D45
SSDEEP96:GbkTBWBPENy/FLlSYjzyx7/NBXBfY+iKF79I:Eg8VW8GxR1aA
TLSHT14E523E2BB7E1D8B3C8696B3C846B4B6A53B5C840437357B32A1814103E937A15EBAB13
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
MD5EE4F685A8202ECB56C55196782CC5AC5
PackageArchi586
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
PackageRelease3.229
PackageVersion0.5_9
SHA-189B5D80B93BEFD77A7F87E099B90450B70D49602
SHA-256E9FBDA0A20252EB609133C6F3F07F649D6EE59561106443767E2E151E2ED0BF8
Key Value
MD52AC6AB4CF56E9F4F2E31DC5125E7A776
PackageArchi586
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
PackageRelease3.228
PackageVersion0.5_9
SHA-1B6D4D4EF06DD7E4915D317EF918D62EDD321DA93
SHA-256F0F3DAB40DAE5B5E108D4A1A9825EC005D609B58FB841D8237389EAFBE3C33F2