Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	913028
MD5	81A08FD7C560D1D3E8CEF8B1C12C0FB5
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://hesweb1.med.virginia.edu/biostat/s/Design.html
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.11-1
SHA-1	97279B8168FFF4E1AD11C82E9227F714A1F52051
SHA-256	F9F03A4D29267287B328C98093A413178E7E7D419DBE446752F1B200B5517CB1

Key	Value
FileSize	910836
MD5	00399CDC37A658BE765AFE19CDFAA510
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://hesweb1.med.virginia.edu/biostat/s/Design.html
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.11-1
SHA-1	25AD6BF400D0892AA0E2E26EFC37BB2421A282F9
SHA-256	D591C63C7B0FCCD5101066502E53753F5D3596A8941DB6936F93389FD684AD89

Key	Value
FileSize	910970
MD5	885EA4452D9BB9FBCFD2793F8787C643
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://hesweb1.med.virginia.edu/biostat/s/Design.html
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.11-1
SHA-1	1BC315A0B24BFCEB91C261FF516D3529229E4890
SHA-256	1D3F1C0D90CB916CD3C4AD9E31995B2A8A69BDB19E928A80980744EF72EE55E7

Key	Value
FileSize	911936
MD5	C79F7752EECE1E3F21A8401545B03BB8
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://hesweb1.med.virginia.edu/biostat/s/Design.html
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.11-1
SHA-1	0CDD8799030D46D9C398C28067859A576D438C79
SHA-256	307D42AD4F42B9FBFB9948D2BD1AAEB1D82E849105E750D737C5F4067334C676

Key	Value
FileSize	915576
MD5	F9B93CF9F984D613CBCB532D179AB8D8
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://hesweb1.med.virginia.edu/biostat/s/Design.html
PackageMaintainer	Dirk Eddelbuettel <edd@debian.org>
PackageName	r-cran-design
PackageSection	math
PackageVersion	2.0.11-1
SHA-1	DED0A41F2FF37BD2A4EC96D15A337EA7B5E1FAB9
SHA-256	43582303CC96F489AC248B5E6A226064E0D2987581F6DF701E21C05FE9F2FBFA