Result for 947969619E10E61413C1390958C21729A1B878EA

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/CITATION
FileSize569
MD544031D59B758358219CD72E0402FA90D
SHA-1947969619E10E61413C1390958C21729A1B878EA
SHA-2565CA345C0DCA2652F7DA8798F491C20BCFF4ED523ECF552A755A9460F7FBD2BAC
SSDEEP6:CZwVleNqq4htKsFMERmhsKMbQHCQh1zqRcPehxLcC3C2BEjcNcmLLVU29IrJkLF1:MwVo0tKs3U6ICQmRe+RBJvLL6pJCKRK
TLSHT154F026978511512733916D0CAF1A50847B2BE1B36C013063B5ECC31C4F2C0DE80E334A
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
FileSize121432
MD5A51B596DEDA07266B463EF3BF1A2072E
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
PackageVersion0.6.2-1
SHA-177B34888449AD1E2479A346EBDE5200ADEB40CA0
SHA-2564471C33A34C373276DB28C0312AD85A8308A5D86763B404848FE12CEEFAE67D6
Key Value
FileSize121052
MD593268611CB2A6AA6479ABDA19A638403
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
PackageVersion0.6.2-1build1
SHA-1442A831BCCB759EF1A64DE04E7FCCCF35AF29C0C
SHA-256ED96C7ED6375CE3F03F2F22BA4F7645C84BD6707F9A6B32C68EA306187E94E02