Result for 504CEEDFA29C62EDC0B0A5E0FB6633CFBF331631

Query result

Key Value
FileName./usr/lib64/R/library/brglm/DESCRIPTION
FileSize1561
MD52D543B330BB5D8FC49DB1E99B834A52A
SHA-1504CEEDFA29C62EDC0B0A5E0FB6633CFBF331631
SHA-2569CBFB027D0EDFF51380503AFC3F93ABB41C73CA91E2B5F40AD6674E2309EFB36
SSDEEP24:O1KKaTLCA0IaWa1hfRrovMDhMjrmCf9cp8tJcZ2wRlpOm/A0JrMoEn:ODWhFT8ovMDhMD+kKZtwmIVnn
TLSHT18931867335E1BB6E0F4025D7BE3F32994A186B7677E2009D182D466C9D0904306C36ED
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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
MD50286855345E1B2A8E09FEFF96AB19FCB
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
PackageReleaselp153.1.1
PackageVersion0.7.2
SHA-127745A2702479F4C311FA2E69524E2536D950E53
SHA-256D7F60F07C295DA20CC1A5D641CA14ACDFA8D623E998221CB22D2340921AC2C5C