lattice, quantreg, nlme, rpart, polspline
Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model
design attributes in the fit.  rms is a collection of about 225
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.

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.

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.