Result for 33BECAF038462586ADC131B38655860621732008

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/Meta/package.rds
FileSize1117
MD5FB6E5F27044A5501101FE2D1F43FF813
SHA-133BECAF038462586ADC131B38655860621732008
SHA-2561A8812AAD866AA3C4E7A3386F90FA702FB16CED29437401D5E216B2B9E166C45
SSDEEP24:Xd3VcU51DODatSb+pkqGAjU3LfICiFOp1JGaTIPy:Xd3VcU51DOetc+pzI3LfJSEN
TLSHT1E621932F0B4F9AB645B47821F236903439483964AC8229DAFEE6730875CD394394E0AB
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