Learn R Programming

⚠️There's a newer version (6.8-2) of this package.Take me there.

rms

Regression Modeling Strategies

Current Goals

Implement estimation and prediction methods for the Bayesian partial proportional odds model blrm function

Web Sites

Overall: http://hbiostat.org/R/rms
Book: http://hbiostat.org/doc/rms
CRAN: http://cran.r-project.org/web/packages/rms
Changelog: https://github.com/harrelfe/rms/commits/master

To Do

Fix survplot so that explicitly named adjust-to values are still in subtitles. See tests/cph2.s.
Fix fit.mult.impute to average sigma^2 and then take square root, instead of averaging sigma
Implement user-added distributions in psm - see https://github.com/harrelfe/rms/issues/41

Copy Link

Version

Install

install.packages('rms')

Monthly Downloads

27,322

Version

6.0-1

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/harrelfe/rms

Homepage

https://hbiostat.org/R/rms

Maintainer

Frank Harrell Jr

Last Published

July 18th, 2020

Functions in rms (6.0-1)

Analysis of Variance (Wald and F Statistics)

Buckley-James Multiple Regression Model

BCa Bootstrap on Existing Bootstrap Replicates

Compute Predicted Values and Confidence Limits

rms Package Interface to quantreg Package

rms Version of glm

Bootstrap Covariance and Distribution for Regression Coefficients

3-D Plots Showing Effects of Two Continuous Predictors in a Regression Model Fit

Function Generator For Exceedance Probabilities

Resampling Model Calibration

Cox Proportional Hazards Model and Extensions

Fit Linear Model Using Generalized Least Squares

Plot Effects of Variables Estimated by a Regression Model Fit Using ggplot2

Fast Backward Variable Selection

General Contrasts of Regression Coefficients

Generate Data Frame with Predictor Combinations

Compose an S Function to Compute X beta from a Fit

Continuation Ratio Ordinal Logistic Setup

LaTeX Representation of a Fitted Model

Logistic Regression Model

Calculate Total and Partial g-indexes for an rms Fit

Kaplan-Meier Estimates vs. a Continuous Variable

plot.xmean.ordinaly

Plot Mean X vs. Ordinal Y

Total and Partial Matrix Inversion using Gauss-Jordan Sweep Operator

Logistic Model Fitter

Intervening Event Setup

Distribution Summaries for Predictor Variables

LaTeX Representation of a Fitted Cox Model

hazard.ratio.plot

Hazard Ratio Plot

Draw a Nomogram Representing a Regression Fit

Plot Effects of Variables Estimated by a Regression Model Fit

plot.contrast.rms

plot.contrast.rms

Nonparametric Survival Estimates for Censored Data

Summary of Effects in Model

Predicted Values for Binary and Ordinal Logistic Models

Miscellaneous Design Attributes and Utility Functions

Predicted Values from Model Fit

Plot Effects of Variables Estimated by a Regression Model Fit Using plotly

Cox Predicted Survival

Parametric Survival Estimates

Cox Survival Estimates

Dxy and Mean Squared Error by Cross-validating a Tree Sequence

Resampling Validation of a Fitted Model's Indexes of Fit

Robust Covariance Matrix Estimates

Validate Predicted Probabilities Against Observed Survival Times

Ordinal Regression Model Fitter

Parametric Proportional Hazards form of AFT Models

Variance Inflation Factors

predab.resample

Predictive Ability using Resampling

rms Methods and Generic Functions

Print cph Results

rms Special Transformation Functions

Ordinal Regression Model

Linear Model Estimation Using Ordinary Least Squares

Trace AIC and BIC vs. Penalty

Bayesian Model Fitting and Stacking for Multiple Imputation

Validation of a Fitted Cox or Parametric Survival Model's Indexes of Fit

Validation of a Quantile Regression Model

rms Specifications for Models

Parametric Survival Model

Progress Bar for Simulations

Residuals for a cph Fit

Residuals for ols

Internal rms functions

Residuals from an lrm or orm Fit

Validate Predicted Probabilities

Plot Survival Curves and Hazard Functions

Validation of an Ordinary Linear Model

which.influence

Which Observations are Influential

Resampling Validation of a Logistic or Ordinal Regression Model

Sensitivity to Unmeasured Covariables

Overview of rms Package