Result for 182AFA783D57FCC857DB5A6E758CF6C9545095E2

Query result

Key Value
FileName./usr/lib64/R/library/rms/DESCRIPTION
FileSize1531
MD532E99ADD6C2FE3F5BA5545E5B6E5BE92
SHA-1182AFA783D57FCC857DB5A6E758CF6C9545095E2
SHA-2563445336B789A54BEE09C44439EB69163844CDB19AA6BAE1166C934AB2F7DDC18
SSDEEP24:iKd2iRgEuu9H5VbYtqg4ZTFTO0qOV+2TcwxaWnLh8FXESSgZkqvrkxrUk:FZbYFcDqOE2AxyLh9cHvg
TLSHT116316501B6202730EF4F4097BFB637934725418B7B56CDB66DD6F00D2B4221D13A6AAD
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