Result for 7D1C7EB8819D2E1CE2616DE17FA302FE6E05EECD

Query result

Key Value
FileName./usr/lib64/R/library/brglm/R/brglm.rdb
FileSize43686
MD5AD2D3F1E58E205AA148913EA5826E226
SHA-17D1C7EB8819D2E1CE2616DE17FA302FE6E05EECD
SHA-2561603D47DCEE93D6A5265FDE9B83FA789A8CE79E731F779A67C6129B266C21607
SSDEEP768:zTL3XtysDCJzWHlAZcFfAPbB4tcPlnCDzIgTZon2BeOrTmsbPgOyphnf37cmZLyc:zTL3XtysWJelAaFYPbB4tKpCogTa2BXW
TLSHT1961301BE5B62984D016CE8CA96834AD1B06CADFDCBFC46183D0B437BED1E71019A7095
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
MD505CCCA38069C76223CDC895DEFB13365
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
PackageReleaselp150.3.48
PackageVersion0.5_9
SHA-1507D00E665869828A39E7157C279194FF8FB1918
SHA-2560EE733EAF24F3DB28775BADB6FC9BA4AD4CB066AB71F0DD2BAF01389F2F4E350