Result for 6564350A74D25D543D91718BBB80121E03F39D6C

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/Meta/links.rds
FileSize345
MD5F599AE69AA05FB6440BC5FECD8984844
SHA-16564350A74D25D543D91718BBB80121E03F39D6C
SHA-2561752FA43EA7C3C6FB2854FAEB94D6B6A17CEAA4A6B550A9356BF53BBD64DEDA1
SSDEEP6:XtVHdNQRUdUFPeiPUDHon5MqN92mePUxCjSHe/mqzZrEcXrVhydMQ732:XKmicD+VNXeY+melrvm32
TLSHT100E0C0644C76E16DCBA094194815354E0CC897026408E424E6835288354BC2B29347B1
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
FileSize90772
MD5015DA8FA008BCA0D94173E8E00CE3C35
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
PackageVersion0.6.1-1
SHA-1846DAA5288933FF2F6E59532BBEAE13431DC3747
SHA-256096D2F0659D1F082663C33702EABD5983288BF8F1E50DC1DF69AA91A64917F08
Key Value
FileSize90944
MD5D3AA50464CEBC1542ACA0D3C5E80273C
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
PackageVersion0.6.1-1
SHA-1B9D99EB521752C8E1D3536E7877946FF9A3FF709
SHA-25609ECBBE346D9C74D05D8E67B59D35E64D7B06968736A012CCACCD17757F7FE75