Result for 3B85C8CAD29B99EA84008ADD56E9E7497FFFEA69

Query result

Key Value
FileName./usr/lib64/R/library/rms/Meta/package.rds
FileSize1293
MD5F8F1770E361638D0C936A8EC7EDECBD3
SHA-13B85C8CAD29B99EA84008ADD56E9E7497FFFEA69
SHA-25695D1E26CFBA5C0A5BF0465E8DE0E72EB745818CE05F7A4468FF139089B5AF0F9
SSDEEP24:X91Hhw+RELlKRzzJTe08q6U/P8MlRQUZOwkcMsbLo/3U0DWgqAlLtYAEn0ZDBi:X9Z+8rNJq08q6U8q0AJLo/kdgq8LtsS4
TLSHT148211DA3067150D2F810D1F0CC1915E0D02FFD6DB64CAC1E19350FCE18C7014111B27A
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
MD51F9C7D9A6308A34E11AC5CE25C988591
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
PackageRelease3.15
PackageVersion4.2_0
SHA-17F77FC346B3C55371E7704A4F68C69A5CA7A2203
SHA-2565A7D89DDEE182186ACAD15913AC850A2C7E265A90938BF37B9EBFF7D3680FA19