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	660A40A12C958E4DBA3D24B33BB3FF3F
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	lp151.3.62
PackageVersion	4.2_0
SHA-1	B1A4CA855016D7752C55CEDA803230F04F0B29FB
SHA-256	F442F63A48ADFB22F54D4EC7D95917DB480668097666625CD8F5EB74FC856C5F

Key	Value
MD5	AF9587D97A15C182A046040EA5267518
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	lp152.3.12
PackageVersion	4.2_0
SHA-1	7927C895A70F147BA8C0BC1CED8CBC5CF15099C9
SHA-256	4724EA59B3317C44932FE6F1B35F944E1EF8CD58D107B22E9C325E06F2488598

Key	Value
MD5	C990591E1715898CDEE60D25D57B4D32
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	lp153.3.13
PackageVersion	4.2_0
SHA-1	0469544E8FE0786B360396EA3B76391D1555A1A3
SHA-256	054BFAB29950A86A3F7D0FFCB7884CCE57EDBE042757E26A4045F4BEFFD297F2

Key	Value
MD5	57F4A7E94DD9F8F1EA5450685F20F8DB
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	lp150.3.49
PackageVersion	4.2_0
SHA-1	09DBDDFCC897C1EDB34FC59C086E4987594F35EB
SHA-256	62263B78790BAF7BFC51C36AAECE5316A36AA4415DB6C6614EA6138ED67CE496