Result for 26C1211D376D5BAA4EBC150172B8346F533385EF

Query result

Key Value
FileName./usr/lib64/R/library/brglm/help/brglm.rdb
FileSize61314
MD514B48FB6D1F63436D315AB711EF1E3DE
SHA-126C1211D376D5BAA4EBC150172B8346F533385EF
SHA-2567B98DA457444E1B831F92A91ED1EE10B179A40942D61AE8196CF886BD28EBD37
SSDEEP1536:/9dEp3xU3siTrhqK3zIlvMj3MNsrNz4aZHx3i0y3Z6:1dEfU8ghqAIlvAesrNz00y4
TLSHT1345302C2A2BAC9AA88545BD8ED01374EE3E13020F722A9E1CA19545EFCD51DFB311D5F
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