Result for 25A966F6C9291CFC5888043A59D9E062BC5E07D0

Query result

Key Value
FileName./usr/lib64/R/library/rms/Meta/hsearch.rds
FileSize4259
MD5092B5F461E7B1AD5D36BD58EDD3C8D7A
SHA-125A966F6C9291CFC5888043A59D9E062BC5E07D0
SHA-256D17C4B3A8E118EBBC5ADA9536217F6736EA135715BE07E403A96F01D56E94B64
SSDEEP96:f+EEE53epb16hX6yBjA+Qmo3NhU0yudz1gc2fMRY+JudjVDmygENtSLNnD:mEEE5OH6hX/R8m2bU0dAfMsniygLLZ
TLSHT101918E32BA5403A7B761F0E58C8A0A9E1C345D3DC1609724886EC40B17F6F07D9CD17D
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
MD5FCE163A75946A60B04651CC161DA4277
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
PackageReleaselp152.3.20
PackageVersion4.2_0
SHA-141432BF47B6CA61B6B82732E675F8D2A1522E688
SHA-2567392F255E6F0A112A3E34EAA3BA86ADD3E5E1B6FB55F2B521C269BB16F27E6A6