Result for 6309AC107206E1C94BFD1C080341F09A733E96E0

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/DESCRIPTION
FileSize1327
MD57B290011F982F515B4755A79F7CA1E10
SHA-16309AC107206E1C94BFD1C080341F09A733E96E0
SHA-256DAF623F42179D87A93830BF49E98D16BCE39E689315F1F07BC7164EC32F306F1
SSDEEP24:O1CeccOA0fRrovMDhMjrmCf9cp8tJcZ2w0gxrsAt:OXccO5ovMDhMD+kKZist
TLSHT16B2183A674817F7B1F4109877B3B27E19A2D06F063F34059201C5A1C6D504A212E32FD
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
FileSize89254
MD58A9014CBE27D6AB2CEF6587DD36FDC18
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 Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.5-9-1
SHA-102EB0194C9224AB83725A17C4622369881F403BA
SHA-256D472F5F0D7A348AD0EB160291D6A3F8BCD202DA637FDEA86765C613331238027
Key Value
FileSize89408
MD5E3E7A77598DAE08B6B4FD8EC33A58D78
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.5-9-1
SHA-1C0972C5E076B8CA046E761CC3957390506A751F7
SHA-256F325AA5C10E9696E49E27C4E14D8E2399F465E0A74C05DD13DEF642BF0F1AAC8