Result for 08208908695784341F9EEBBFD13FB1014696AB44

Query result

Key Value
FileName./usr/lib64/R/library/brglm/Meta/package.rds
FileSize1122
MD5D53E2348E8D4F4511A6D6C9B75C67B94
SHA-108208908695784341F9EEBBFD13FB1014696AB44
SHA-2563D09A80FE965E63B9063AC672FF6B8CC1164AF36826969837483772BE46F04F9
SSDEEP24:X7Zzf25XPvT8IzyT9CX+Jaqy/M33gnEMZmw3Swd7VeyTyJ7uq12ynWTL8nnzUU9:XpqXPvT8I69COoq3YEMwwxQeT4j
TLSHT1C521F9156B6187C19B809F03318BFA3E48F41D16F0E67D2FFE718A021F060880866C4C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5D4C0DC4BA189451015B0104B2D977607
PackageArchx86_64
PackageDescriptionFit 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.
PackageNameR-brglm
PackageRelease3.6
PackageVersion0.5_9
SHA-1C6F95E0AAD3BF15B5ED57CA79B53BE611EDDA51F
SHA-2560F357B9BFF78AF0759482FB6C494DB27D4570B648920042F4403F9F674377640