rms: Regression Modeling Strategies

Regression modeling, testing, estimation, validation, graphics, prediction, and typesetting by storing enhanced model design attributes in the fit. rms is a collection of functions that assist with and streamline modeling. It also contains functions for binary and ordinal logistic regression models, ordinal models for continuous Y with a variety of distribution families, 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 regression models, 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.

Version: 4.1-3
Depends: Hmisc (≥ 3.12-2), survival (≥ 2.37-6), SparseM
Suggests: lattice, quantreg, nlme, rpart, polspline, multcomp, boot, tcltk
Published: 2014-03-02
Author: Frank E Harrell Jr
Maintainer: Frank E Harrell Jr <f.harrell at vanderbilt.edu>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://biostat.mc.vanderbilt.edu/rms
NeedsCompilation: yes
Materials: README NEWS
In views: Econometrics, ReproducibleResearch, SocialSciences, Survival
CRAN checks: rms results

Downloads:

Reference manual: rms.pdf
Package source: rms_4.1-3.tar.gz
MacOS X binary: rms_4.1-3.tgz
Windows binary: rms_4.1-3.zip
Old sources: rms archive

Reverse dependencies:

Reverse depends: contrast, CPE, FeaLect, LCAextend, lordif, nonparaeff, pec, pqantimalarials, riskRegression, Tsphere
Reverse imports: Daim, greport, riskRegression, VizOR
Reverse suggests: bbmle, catdata, gap, haplo.stats, Hmisc, languageR, ModelGood, perturb, rankhazard, riskRegression, shrink, survAUC, SvyNom