Result for 46AB6FC35BDAA33C204503BD150950C7EBD474DE

Query result

Key Value
FileName./usr/lib64/R/library/brglm/R/brglm.rdb
FileSize44201
MD5E10DC4C110C07B2AF8C208E31C989D1D
SHA-146AB6FC35BDAA33C204503BD150950C7EBD474DE
SHA-256A3474B791F6E322A88D0DCA7EB3A0EE4F83B70E8C2A441BF9E6C5F2F8D16643F
SSDEEP768:BQJLVe1MQLaP7wIIGbodOuF2rpgfKm2XYxDMH9SHtc5/GRRZhRKMkOqdl4pseOEn:ehVMLw7w8kdOuFSpguXYxalkfKMkOqdk
TLSHT1F413F14E0A986A6FC201ADB4C2CD79A4B2B7D5D71CCB6E5384384ECC47DE98C6272375
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
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