Result for 231737D342DB5BAF216A53650611D6AD141196F6

Query result

Key Value
FileName./usr/lib64/R/library/brglm/R/brglm.rdb
FileSize43680
MD576B804CB62DFFA239ACE45CFEF27DA93
SHA-1231737D342DB5BAF216A53650611D6AD141196F6
SHA-256861AEBFCB25E275093592388F742E370BC193B4D9AF5ACD66BC6423479929B12
SSDEEP768:zTqZ3XtysDCJzWHlAZcFfAPbB4tcPlnCDzIgTZon2BeOrTmsbPgOyphnf37cmZL7:zTy3XtysWJelAaFYPbB4tKpCogTa2BXW
TLSHT1A613F1BE5B52984D027CE4CA85874AD2B0689DFDCBFC86187D0B433FED1E711199B095
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
MD556AADE7A867F871B437C0FA66E4EA5CB
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
PackageRelease3.26
PackageVersion0.5_9
SHA-1B1D6D8B82AB45FB309F967FC9FB80DE82CE23447
SHA-2565C80E2EBB1E917A441B857D1156C799E9953EB1AF326A8E0A20BFF0D08437C3E