Design (2.3-0)

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Design Package.

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, the Buckley-James model, and generalized least squares for serially or spatially correlated observations.

Maintainer: Charles Dupont
Author(s): Frank E Harrell Jr <>

License: GPL (>= 2)

Uses: Hmisc, survival, survival, nlme, rpart, tree
Reverse depends: contrast, CPE, FeaLect, LCAextend, LMERConvenienceFunctions, lordif, nonparaeff, Peak2Trough
Reverse suggests: gap, haplo.stats, Hmisc, languageR, LMERConvenienceFunctions, pec, survAUC, survcomp

Released over 10 years ago.

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