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.

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Description file content

Package
rms
Version
5.1-2
Date
2018-01-06
Title
Regression Modeling Strategies
Author
Frank E Harrell Jr
Maintainer
Frank E Harrell Jr
Depends
Hmisc (>= 4.1-0), survival (>= 2.40-1), lattice, ggplot2 (>= 2.2), SparseM
Imports
methods, quantreg, rpart, nlme (>= 3.1-123), polspline, multcomp, htmlTable (>= 1.11.0), htmltools
Suggests
boot, tcltk, plotly (>= 4.5.6)
Description
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.
License
GPL (>= 2)
URL
LazyLoad
yes
NeedsCompilation
yes
Packaged
2018-01-07 18:46:22 UTC; harrelfe
Repository
CRAN
Date/Publication
2018-01-07 22:27:43 UTC

install.packages('rms')

5.1-2

9 months ago

http://biostat.mc.vanderbilt.edu/rms

Frank E Harrell Jr

GPL (>= 2)

Depends on

Hmisc (>= 4.1-0), survival (>= 2.40-1), lattice, ggplot2 (>= 2.2), SparseM

Imports

methods, quantreg, rpart, nlme (>= 3.1-123), polspline, multcomp, htmlTable (>= 1.11.0), htmltools

Suggests

boot, tcltk, plotly (>= 4.5.6)

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