Result for 0B862231BBB77E8BCACD83ECF10FFDDD0AC34DB2

Query result

Key Value
FileName./usr/lib64/R/library/rms/Meta/Rd.rds
FileSize4103
MD56B19FB971E663CD625B24F78FA89FB7A
SHA-10B862231BBB77E8BCACD83ECF10FFDDD0AC34DB2
SHA-256D72207E23346E794DC204F012A41474315A47FD6EF0C6953A5A833E221A5ADF3
SSDEEP96:d/mPpgHSJHtomxyf7TTZfVDeXZZufAHS+pwj3clljzzqKzV9:tmhg8/WXTl4fLSj4jyKzv
TLSHT1D9816D4C47DAD540EA4BD29E88664277673867ACF960FCA1E30AEC15F8D643C7AE4484
hashlookup:parent-total1
hashlookup:trust55

Network graph view

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