Parents (Total: 9)

The searched file hash is included in 9 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
FileSize	1989544
MD5	2B628D06968A0A145682BAE2A1EB26BB
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	5.1-3-1
SHA-1	738B374D21C0056D33B4CA81303B5472B494ACA6
SHA-256	DDB020EE6A675A4F2DD7C2035786B9E27C891DC35947BDE9B78B1DEE98D02A78

Key	Value
FileSize	1992728
MD5	7DAE39E1F1C85E97332279E4F2B88F55
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	5.1-3-1
SHA-1	BFD5E28BEAFCB0477ED14154BCBFC87C64046BAF
SHA-256	8FA011C016B28219A90C2DA567EF4614916ACBDD86A78FA26E4E7659DE4F85B4

Key	Value
FileSize	1988832
MD5	E218B356420A20FA5112F7EB72F90395
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	5.1-3-1
SHA-1	CF07759CF170281065A49F745B6D4E70EEB1BE7D
SHA-256	2739108957E090FDB5C58C813B68EAB44D29B3E2DB1D11BF4634611C42BAC4C6

Key	Value
FileSize	1990192
MD5	E0F261619592C96548905A3320546135
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	5.1-3-1
SHA-1	9973C6DBEA5121194EAE4B15C25357E72B696177
SHA-256	27AC3140A2EE37F4AE1CF8709CE6C4DFF48057E647CEF7551C3FBB6A2B365A0D

Key	Value
FileSize	1989408
MD5	E249C2B93EFF89AC47580ED0180B1D33
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	5.1-3-1
SHA-1	9EE530F1ACAE29022A975CAA398D12CF8038527C
SHA-256	62046B0773DAF1EDCFF548C01F48731C939F8792A947953F61F49273FF7053ED

Key	Value
FileSize	1989420
MD5	8828FAAEFE4C06DE09CB0B80828A03B4
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	5.1-3-1
SHA-1	54ADAAF08F723CDD3FCB5105520AF902B96FB1E2
SHA-256	3D8DEC21D74D3FA9EF5F65A4939F8C9DACB85BCDDB6CA7933CBB70918DD8A48F

Key	Value
FileSize	1990568
MD5	3AF28D8A950C723B0EC92F92794A7431
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	5.1-3-1
SHA-1	0580025E4FFE9EC87A56241B88753553889AEED9
SHA-256	95E24A785836EC1094BE6BE9DE99136BD756ABC3E332B3AB4DAEA22E0C45BC0F

Key	Value
FileSize	1989920
MD5	AA66928E1A090C4C1EA2E0563450AB6F
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	5.1-3-1
SHA-1	E590A17E50E2B88F670FA341C798F189BADD3FB8
SHA-256	9A8B8BE820CD446B8C301A362A748FD54CD52173A43F187470E8C0B4593A4EB3

Key	Value
FileSize	1988008
MD5	4E7652CCA7C4078A8E24BBD0B4576A87
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	5.1-3-1
SHA-1	8EC3E87495BB57491D07149B8E098E2EE2ADF713
SHA-256	63F2F36E9C73C2033F65187AB412152C98A15E750B35859255962AE0C6D691A4