Result for 034A57D277C22E60FE25678F27D9E6354BD8FA85

Query result

Key Value
FileName./usr/lib64/R/library/rms/help/rms.rdb
FileSize665773
MD5758A2FECAEF736B4593165F77E95CD0F
SHA-1034A57D277C22E60FE25678F27D9E6354BD8FA85
SHA-25665A11402A8566E60EA1E56A79F1F30FC172D524999CF7CEA74E03445EECF739E
SSDEEP12288:DBRVAlYnM738cmBUDAindum5hUYNz9H1gavc6v91olgYQtT/+w6tvYfCZuR:DnE7MpB0nBNzx1/E6FKzPvYfC2
TLSHT1FEE423B96E344D447730B70334ABE6C381537879D6408B96E691EC8B1C23574ABBB1EB
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