Result for 06B61F79C91799C2E24996EC336CC40988F69655

Query result

Key Value
FileName./usr/lib/R/site-library/Design/html/residuals.cph.html
FileSize5836
MD5790B2179EB96D6E41BE6D25F7D12F159
SHA-106B61F79C91799C2E24996EC336CC40988F69655
SHA-2568C75641C23BED4C51C86E83091FD2B77B514C6FC6DF9E23639D028904FF32A72
SSDEEP96:O7LeJOFBJWrrDVGHoUPnHlAffImA0DqJd7lO9OHzQdiXOwXIy2SKwxpano60nx:kaJmGLVKlAffDA0DaVBHiy5uwxp4oJx
TLSHT147C1B411EBC81B474C16A0EEF54824C4BB5D412067291AC0FD4FEB7D9749879C3FA6BA
hashlookup:parent-total4
hashlookup:trust70

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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
FileSize909330
MD52230E84B4CA450D666A0C55272F4E6DB
PackageDescriptionGNU 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
PackageMaintainerDirk Eddelbuettel <edd@debian.org>
PackageNamer-cran-design
PackageSectionmath
PackageVersion2.0.9-2
SHA-190755BF824BEFB664860F495B33F6A1E76F27CEE
SHA-256D9DD31E59E89E45ED292B8871EBE0817B2688433213A206C5A14C0C01B804387
Key Value
FileSize911504
MD56B115E7962CD630DCF0B293203BAFF1C
PackageDescriptionGNU 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
PackageMaintainerDirk Eddelbuettel <edd@debian.org>
PackageNamer-cran-design
PackageSectionmath
PackageVersion2.0.9-2
SHA-151018E951F8FFE0AD6473ADC9B263A214DFA0AA8
SHA-256F5474356C67DCE0C5E4B7F001A713F4172B89A508324792B3D4BFE43EC536093
Key Value
FileSize913522
MD57308B3B9E2A515B1B3FC7DE86B44FB3D
PackageDescriptionGNU 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
PackageMaintainerDirk Eddelbuettel <edd@debian.org>
PackageNamer-cran-design
PackageSectionmath
PackageVersion2.0.9-2
SHA-18914C4AFB66B1A8C6940BE3E36E99AEF980886A0
SHA-2565E9D3C5A2B2545B16F19E8A6CE55FB0C4ABF2ADFE9B24F54F8486554311D3825
Key Value
FileSize909480
MD5F822B4A497FF97FEDBE6D3F370B08643
PackageDescriptionGNU 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
PackageMaintainerDirk Eddelbuettel <edd@debian.org>
PackageNamer-cran-design
PackageSectionmath
PackageVersion2.0.9-2
SHA-11423E59114977210AF70E876115A3A3EE1547ADB
SHA-2566D0B466FB991FB4899C049D64E27622035F494DD103C1A792F37A2657D996589