Result for 2A1951367C80A1813D0FE37948A5030DFCAA7E4F

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/R/brglm.rdx
FileSize579
MD506E56F11BEC6F6D157A82725EAAB3CD6
SHA-12A1951367C80A1813D0FE37948A5030DFCAA7E4F
SHA-256BE97D754CC1AB1412DB19069BC5D698C5E06E57DC5FED5E07B9A3CE68A160614
SSDEEP12:XQifwkYY/+tUI4XUjAy/IXsEZOtUMPZH7IYeK7rUZ0eTWKm6EE4ry:XQiI++mIS8AcIXsYOmOljkTqK/f
TLSHT1EFF00C6F2E1A237FDE2699A9E83AAEACE540554E0B2938649D0A428600D1332889D468
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize124056
MD54EB8CE5FB3B905311C60BBFDBDC15E67
PackageDescriptionGNU R package for bias reduction in binomial-response GLMs Fit 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.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.7.2-1
SHA-1ACC29A4290ECA57B4997CEF50352D132349EA702
SHA-25600967E0951EAB21ED06F2B9171D9156D073781E2CC0EAACB186CF5C1C09C55F5
Key Value
FileSize125244
MD5F6C18D2FCA3DB514813C06E6AD7B21A2
PackageDescriptionGNU R package for bias reduction in binomial-response GLMs Fit 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.
PackageMaintainerDebian R Packages Maintainers <r-pkg-team@alioth-lists.debian.net>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.7.2-1
SHA-1EF61D8496674063966A4F60EF4E15AF1C7EA3814
SHA-256DEC1E4D300EF89B18D1515493F4DAFAE0BC112362D7B07806D49919EEEC62E93