Parents (Total: 4)

The searched file hash is included in 4 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	7EF8E71C2FE0C433E8B454FDB63954EE
PackageArch	x86_64
PackageDescription	Regression 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.
PackageName	R-rms
PackageRelease	lp153.5.2
PackageVersion	6.2.0
SHA-1	6DA268D682309499F3798F0AD7BA03F30D3C6F9B
SHA-256	CC64B84165727CF85A4AE1BED162D72D2A77A8F323EB2A2337F8B2BD7CEB7838

Key	Value
MD5	1F87758584D4D4CA5C8EBD371A0F5828
PackageArch	x86_64
PackageDescription	Regression 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.
PackageName	R-rms
PackageRelease	lp152.5.2
PackageVersion	6.2.0
SHA-1	7EE1DC49923FF02D7936A827EFD1F56D473A5374
SHA-256	891916A7DA4CC6CAE9167319945F6EE72820B57AFBC7038341EE5C3D77967DBB

Key	Value
MD5	94CAA81C009EB013DC0BF8E8FBF0D362
PackageArch	x86_64
PackageDescription	Regression 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.
PackageName	R-rms
PackageRelease	lp154.5.1
PackageVersion	6.2.0
SHA-1	8E48B486FF8D3CECA78B5EB610D373A6022235B7
SHA-256	3E38B56B2B58721158F7B681811BDBE22DAA15419D93462467B29234A5B69EA7

Key	Value
MD5	329C15DA67C05D7C7F460256ED85BDE5
PackageArch	x86_64
PackageDescription	Regression 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.
PackageName	R-rms
PackageRelease	5.6
PackageVersion	6.2.0
SHA-1	81D69303771A2AFD6E7478AA6BABF07FC36D47E4
SHA-256	C17A532AE57EC1D88532B5F228FC0ED0B6C1A89A6A2546B8D4F3333CB7E04660