Result for 18822E1DCDD6F8774154497218CA4AF990B8A5FF

Query result

Key Value
FileName./usr/lib64/R/library/rms/DESCRIPTION
FileSize1531
MD562C14541627C64B27F911EEEB8301C81
SHA-118822E1DCDD6F8774154497218CA4AF990B8A5FF
SHA-2567EE32EB520756AAE51857B016D5E7A05BC6BA97D67FC089D1280746AB8B7D9AA
SSDEEP24:iKd2iRgEuu9H5VbYtqg4ZTFTO0qOV+2TcwxaWnLh8FXESSgZkqvrkxra6T:FZbYFcDqOE2AxyLh9cHvk
TLSHT15C316501B6202630EF4F4047BFB637934725418B7B568DB66DD6F00D2B4321D13A66ED
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