Result for 16A54261A592B9A163BA6DC421D85F3B6831ECD0

Query result

Key Value
FileName./usr/share/doc/r-cran-brglm/changelog.Debian.gz
FileSize343
MD5F734DF11DEAC0812B3D517596F133298
SHA-116A54261A592B9A163BA6DC421D85F3B6831ECD0
SHA-25692B1973A254C5049ABF2DF14F0D2361CE56010AB736E87A28668FF9E0B10C997
SSDEEP6:XtdAopXb4D29vgBNvyDxCjzDDPo+8yAg0BY4xBKpNoTc96zHZvsb4:XvbtMD29vgnvydsDM1yt6YmKpNf967ZD
TLSHT192E02D2D40BA20D1AF8C0454816413D8C1B8E13A86C8FDEEA0E8A3394DAC338E86003C
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