Result for 2BF48F93ED99FD16CD6C6C5BC4DF6B88A085096A

Query result

Key Value
FileName./usr/lib64/R/library/rms/Meta/package.rds
FileSize1292
MD579674B431026EAFF2066EFB388B09CF7
SHA-12BF48F93ED99FD16CD6C6C5BC4DF6B88A085096A
SHA-256174789747717F0589287D6FEC3EE8FF24E808FF9BE4E0DA9F5F189C1BD50CC5D
SSDEEP24:XPw8eCpEfttNug8SLgoPa+45kgVGPFGZdakFHET3YcxZ4o82Wir+Vl21X5XS7M:XPwlFfbDUTVX9FHET3YcxZ4QrGQi7M
TLSHT123212BFDAE37E6C584E68D9BE0E2A32BE69488D40F2141644516000F34895CBF4426E0
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
MD5029E024E00386863A56534A52EBE83A4
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
PackageRelease3.19
PackageVersion4.2_0
SHA-1AC8651690C23F60BB8E3DE1474869E82D15362E4
SHA-256BEEE327E0593D3D962D0B528A04CEE2AA5C098F63F6FE2ECF071103FD1305AED