Result for 0E8ECAE27AB0A304D7F73DD9D7B8BC8B67394134

Query result

Key Value
FileName./usr/lib64/R/library/rms/Meta/package.rds
FileSize1292
MD5196FF738FDA050B7640766D2B8F4A8BF
SHA-10E8ECAE27AB0A304D7F73DD9D7B8BC8B67394134
SHA-25605F4B34A8A64E0E2BE568618A2724A3BA617F1CF4A9A6B5376352B6799C7D499
SSDEEP24:Xp2RTXTIUKRzzJTeSsq6U/D8wlfoNGFIkWw6E4p5CHf7VGcuFyt8:XQRTKNJqSsq6hGF7sXOy7
TLSHT13721F8543B2245303E8C5DF0393363FAA80F871F9B64680E41FE33C8280A520B80A10E
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
MD59491CB1FBBEC2D1CA38A7F1330C23C62
PackageArchx86_64
PackageDescriptionRegression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, and the Buckley-James multiple regression model for right-censored responses, and implements penalized maximum likelihood estimation for logistic and ordinary linear models. rms works with almost any regression model, but it was especially written to work with binary or ordinal regression models, Cox regression, accelerated failure time models, ordinary linear models, the Buckley-James model, generalized least squares for serially or spatially correlated observations, generalized linear models, and quantile regression.
PackageNameR-rms
PackageRelease3.6
PackageVersion4.2_0
SHA-112DF2D18BB725E42F161E6C3AF974FF8EF977116
SHA-256FCC49A60F90A92E6F1E289845A18588807B406D367FD262C591EB98155C66F58