rms (3.6-2)

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 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.

Maintainer: Frank E Harrell Jr
Author(s): Frank E Harrell Jr <f.harrell@vanderbilt.edu>

License: GPL (>= 2)

Uses: Hmisc, survival, survival, boot, lattice, multcomp, nlme, polspline, quantreg, rpart
Reverse depends: bujar, contrast, coxed, CPE, DynNom, FeaLect, LCAextend, lordif, missDeaths, nonparaeff, Peak2Trough, pec, pleio, riskRegression, Tsphere
Reverse suggests: bbmle, catdata, dosresmeta, gap, ggeffects, goftte, haplo.stats, Hmisc, insight, languageR, MachineShop, ModelGood, nomogramFormula, ordinalNet, pander, perturb, pubh, Publish, rankhazard, riskRegression, rprev, shrink, sure, survAUC, survxai, SvyNom, tangram
Reverse enhances: stargazer, texreg

Released over 7 years ago.