Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	D363C8DD28B0936DBC691029E46DDB84
PackageArch	i586
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	3.242
PackageVersion	4.2_0
SHA-1	268A63771B47BCBCCC107C890AC589B4A05B904B
SHA-256	8BA5AD16752BE5175949E6B3BB3DBB4701D58B012476EC321F58BFDE5BF189DD

Key	Value
MD5	72A6FED894B1680E2077EA3771A66F6A
PackageArch	i586
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	3.242
PackageVersion	4.2_0
SHA-1	AF227D7D12AFC0B8272BA51F2A579D3BB283F9F3
SHA-256	1DB15A78096D933E3F4566952AB495975D525134E1E8EF8219403188ED865CB5