Parents (Total: 11)

The searched file hash is included in 11 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	914372
MD5	30A9955A0085D477EFC1FDA8EFFFC7C4
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-2
SHA-1	C65B39851C6296EC44512396C63CF62091B8E036
SHA-256	52802F3942917D98F5C2B17463FABF9189B00BABD6669A98BBB63E889CC011F8

Key	Value
FileSize	914990
MD5	643B3B5C1B2B145DB74983574797D407
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	563883B7BDF731FD9E753C6E70823B3CA3ADB600
SHA-256	8E55C73007A5F5CF2F8F5C20CAAF940F612D628E35441524C2161214F2821B89

Key	Value
FileSize	918960
MD5	C5E7820B612A8136D4AE47AF03AB0500
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-2
SHA-1	6AE24643A462E7A30580BFC618391F68EFD6A8AB
SHA-256	08138438A6232078DA27A358BB32BFFD2F6DC9CB9D0C382B4F37E1EAB2E54AC2

Key	Value
FileSize	912914
MD5	A2FB4BB54E2CE6B17CAA1E82A92C359A
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	36181E6BADC656C1ABA089698C98D091AFEDEC77
SHA-256	7BDEB30AA0B48D9C19FD27490383D794BBB03434CC61D7C8B89F9D591F22A202

Key	Value
FileSize	916812
MD5	DE1D2697123C5D5644B22F21C7B1FACA
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	7B11E960F5B1E6877CE91D82F53A1940369DBA54
SHA-256	BE792E7B07E47D35A1D98DA5250FAF6BF26335D4C92A9D0F5710379EB137A985

Key	Value
FileSize	915454
MD5	89F7B20F42C0B34084BEEE969018DFC2
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-2
SHA-1	D1E20DFC53C592777526C3237A39DA61DEFECDA8
SHA-256	FD1F87A389E1124D31DC57961211CBAF61E270F390422C3A532D0EC310542C14

Key	Value
FileSize	914370
MD5	FB5A40B57AEE67B46BA0FA343A8AB5CF
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	2138FFC01A3E5CF46646220422DD82EAC8650D32
SHA-256	37BE65B0E0B3FDD5840829CEEDBB17B7864769DAF8D6447E820BAF9AB7197150

Key	Value
FileSize	912974
MD5	AA553CF66D7D81A874AC44114B0CFC5E
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	5D754D3C38550A3C5F5FA1CB35EAD269578B4E60
SHA-256	7943498FD73B7C20346BBA43DAB3A82F207296957CD1CD4A2090D45F7B350D30

Key	Value
FileSize	922362
MD5	AE0126E46B9C1153AEF2A76DEAE9B697
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-2
SHA-1	23061281D7A3571F2128E0D07C47E05F0695EC29
SHA-256	D502CC6CD15BF1EA31ECF6BCE7CD667EB7D058251CEA99F187EFB9AA3157AC17

Key	Value
FileSize	916102
MD5	3DFD0579BD8FD31BA23C2A321B4F9E3A
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Ubuntu MOTU Developers <ubuntu-motu@lists.ubuntu.com>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-2
SHA-1	ED476196E3F269BE84D321DB8907625A42CE0F26
SHA-256	452DD58BB63035A034101F94F776AB090DB3DE530E0ECA31EA72BBC867F48D8C

Key	Value
FileSize	913898
MD5	361018E941B2426740D0E70DEE1E68EA
PackageDescription	GNU R regression modeling strategies tools by Frank Harrell Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. Design is a collection of about 180 functions that assist and streamline modeling, especially for biostatistical and epidemiologic applications. It also contains new 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. Design works with almost any regression model, but it was especially written to work with logistic regression, Cox regression, accelerated failure time models, ordinary linear models, and the Buckley-James model. . See Frank Harrell (2002), Regression Modeling Strategies, Springer Series in Statistics, as well as http://biostat.mc.vanderbilt.edu/rms and http://biostat.mc.vanderbilt.edu/s/Design.
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.12-1
SHA-1	4DD1D496A54390A68A064148F89D5040EE4DC72F
SHA-256	E240007919C2EA36756BDE263F12C0E576B908342E6C94CA184B478AD8617336