Result for 1478FB4E70760AFBB6D4D1A5D2A7B0E2FD0841B9

Query result

Key Value
FileName./usr/lib64/R/library/brglm/Meta/hsearch.rds
FileSize916
MD5569E8E8BD338FFBF61C549074DF3ED05
SHA-11478FB4E70760AFBB6D4D1A5D2A7B0E2FD0841B9
SHA-256EA9ED41A92FBA5505586752D08105D2C7EADE8BDE9D3C1F8E483580CF1743101
SSDEEP12:XtohtWkEzQCK8NgzmIhwMg9IaJmCBCZ2hZDewDlUxoib8DyhI7Ylp7NCMI87U/jD:XtKQpKZ9g9IomwaCZDdDlUBIu7kfZjD
TLSHT14511634C744DBB334643F62E9360C8183FC999180B5291B5D6AF93DBA87D93C79C12C5
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
MD5C4C88F8E0463F6B15E01ABA7A8687134
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
PackageReleaselp152.3.19
PackageVersion0.5_9
SHA-1E51D016F37B50EE0EBDA0A6A6C40A4052ACC624F
SHA-25648896505E655F4BA96A080FCEFC4237F0C2FD74921011AEC948F45BB59206586