Result for 88A3C9C79D1DC148A6E9F3606E1081C7BA3EF40D

Query result

Key Value
FileName./usr/lib/R/site-library/brglm/R/brglm.rdx
FileSize541
MD5FC2768B02FD1E1E671B51DA4B7D9FB7C
SHA-188A3C9C79D1DC148A6E9F3606E1081C7BA3EF40D
SHA-256745DF11F16D9402F799947D3E0852BBA8FC169F2A31E8534674BA3D808BE4090
SSDEEP12:Xj12Rd/tmS/EfrxoGjdVHbLQtsjdR5v8A64Tk40uRSV7X7plGIs4xXXCIv:Xj12RmMwFoGjdVgudRZa40uR4nplzxXj
TLSHT15DF02025868DF01CB4B9A37225B2A914D3D883BF23B0B70300F651985EC8D414E2200E
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
FileSize89264
MD5C687EFE51317BE1C4CEEBEF64E299A22
PackageDescriptionGNU R package for bias reduction in binomial-response GLMs Fit 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.
PackageMaintainerDebian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.5-9-1
SHA-10164AD6A590499B88DF3A586EAE09C38BA988C23
SHA-256FB8D499AD325D89763FF545941AE34E6656F36FA7E57AD75489A6F366C6B51A6
Key Value
FileSize89408
MD5E3E7A77598DAE08B6B4FD8EC33A58D78
PackageDescriptionGNU R package for bias reduction in binomial-response GLMs Fit 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamer-cran-brglm
PackageSectiongnu-r
PackageVersion0.5-9-1
SHA-1C0972C5E076B8CA046E761CC3957390506A751F7
SHA-256F325AA5C10E9696E49E27C4E14D8E2399F465E0A74C05DD13DEF642BF0F1AAC8