Result for 0B9A8A4E5126DD3CDB497A7D3B7F9E78E6FBCCCA

Query result

Key Value
FileName./usr/lib/R/library/brglm/R/brglm.rdb
FileSize14410
MD5FD5C95D9FECC860EA78A31AE8A535582
SHA-10B9A8A4E5126DD3CDB497A7D3B7F9E78E6FBCCCA
SHA-2562C02EA3BA8C07C84EA94678710276EB8FDB86D1871169C92777FB398E6494A85
SSDEEP384:3/XIYGxlrEmqgpLRzrk9SFNApZDHXbXCKRUZx/yS5oNud:v4Zxd5qg3k96NAnLXbXrOZx/l5d
TLSHT1B352C0584DE5F46D0FA2DC8C907F37589322C8A34CBCE822635643F81A6D5906ADF7B9
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
MD51844F770F3A391FEE4FFF98594E3E7E0
PackageArchi586
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
PackageReleaselp150.3.5
PackageVersion0.5_9
SHA-1054928618816F0338B95569E9A4ED25664599750
SHA-256F02413D347C51B95A250E1891811782A9FCF93D50541B34F58F0A09E058C6BAC