Parents (Total: 19)

The searched file hash is included in 19 parent files which include package known and seen by metalookup. A sample is included below:

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

Key	Value
MD5	9491CB1FBBEC2D1CA38A7F1330C23C62
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	3.6
PackageVersion	4.2_0
SHA-1	12DF2D18BB725E42F161E6C3AF974FF8EF977116
SHA-256	FCC49A60F90A92E6F1E289845A18588807B406D367FD262C591EB98155C66F58

Key	Value
FileSize	994642
MD5	A461664C6D207D06A5734E37BCE4BED7
PackageDescription	GNU R regression modeling strategies by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of 229 functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models 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 logistic regression, 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. . See Frank Harrell (2001), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/Rrms.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-rms
PackageSection	gnu-r
PackageVersion	4.2-0-2
SHA-1	1699F39177BAA7746AD892E4BAEECBD3363E7553
SHA-256	41088618A9F6B27C3530D4A7ED33E2F1F85AF9AE9B07164F2DA71FE1E4B9FE87

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	E806ED76954656E07B441DF9F3ABB6A5
PackageArch	armv7hl
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	3.192
PackageVersion	4.2_0
SHA-1	30070ED7294922AAFDE3A33C8600D9A3FA3F6D86
SHA-256	B7AB02821D252A1DDC3F597393923102782FB7CF0860C64054434DFC46DC96A5

Key	Value
MD5	28A15E33D0AEADBDB452B2385D3F747A
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	3.242
PackageVersion	4.2_0
SHA-1	3DC40FE2EECF4A9A04B33097D91EC75839E49170
SHA-256	35301A0364AE8DF7317411E8881CA2176F336ACE55BB98F9ED4F087081A9854F

Key	Value
MD5	FCE163A75946A60B04651CC161DA4277
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	lp152.3.20
PackageVersion	4.2_0
SHA-1	41432BF47B6CA61B6B82732E675F8D2A1522E688
SHA-256	7392F255E6F0A112A3E34EAA3BA86ADD3E5E1B6FB55F2B521C269BB16F27E6A6

Key	Value
MD5	CEB8EB4F81B65E958D5928F63D48349B
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	3.242
PackageVersion	4.2_0
SHA-1	4FA733A5BF74F994D1A44DB4E531317889BB4005
SHA-256	5AF4D15E3040354D0DB89E46A36801D99C17B022282F973AE14781385453FA32

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