Result for 73F5B159FFB9DAC0F8702812B05CE4E5820483DD

Query result

Key Value
FileName./usr/lib/R/library/brglm/html/00Index.html
FileSize3954
MD574E1F02CE0690E8C7BAB489237E66DAC
SHA-173F5B159FFB9DAC0F8702812B05CE4E5820483DD
SHA-256E7307B499A088DF4D61AC60832AC02210B610697E90FA8B213032F87396CD2A7
SSDEEP96:1zWK4pYQdJQPrdi8QufkCXwxQcGQlhsUMtPQKg:1mxU7FIhBKg
TLSHT13C81AED381C5697E424119A9A7A53CEE2AE103F467465D109A7F6CFFDF027E282531C3
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
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
Key Value
MD5C4C88F8E0463F6B15E01ABA7A8687134
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
PackageReleaselp152.3.19
PackageVersion0.5_9
SHA-1E51D016F37B50EE0EBDA0A6A6C40A4052ACC624F
SHA-25648896505E655F4BA96A080FCEFC4237F0C2FD74921011AEC948F45BB59206586