Result for 04CBD91B8599126B04DE6219DC964A43E43109C5

Query result

Key Value
FileName./usr/lib/R/library/brglm/R/brglm.rdb
FileSize43679
MD533F1F9BECD0C8039211DEC9A2D9F3B5A
SHA-104CBD91B8599126B04DE6219DC964A43E43109C5
SHA-2564D5021D15414B34780BA0FB0C98A4C3F7D155518380FA709DA0088D0B3AC06FE
SSDEEP768:zTSO13XtysDCJzWHlAZcFfAPbB4tcPlnCDzIgTZon2BeOrTmsbPgOyphnf37cmZn:zTSq3XtysWJelAaFYPbB4tKpCogTa2Bm
TLSHT12313F1BE5B52984C016CE4CA85834AD2B468ADFDCBFC86187D0B437BED1E7101DEB095
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 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