Result for 5909978182A3DAD9DF36DE1E2AA275299B081A15

Query result

Key Value
FileName./usr/lib64/R/library/brglm/libs/brglm.so
FileSize7480
MD52D2F6AAA6B87C0DA586ECDFC8004A2F5
SHA-15909978182A3DAD9DF36DE1E2AA275299B081A15
SHA-256D63C766CAA46D43180C114BD6451B7FAE830BF71385DE7CB08792385BD5D3E49
SSDEEP96:RPMBWBcE2LplReY+v3U7pYBhBS+A+k3G:R08CE2LNeYuUOL/A
TLSHT193F1DA5BFAE2CD2FC46A13B8402F177173B1D495125393236A04FA753D43AE82EA16DA
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
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
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