---
pagetitle: RMS
title: "R `rms` Package"
author: "Frank Harrell"
date: last-modified
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toc-title: Contents
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# [Regression Modeling Strategies](/doc/rms/book)
## News
`rms 6.8-0` has a non-downward-compatible change to the `orm` function that improves how unique numeric values are determined for dependent variables. Previous versions could give different results on different hardward due to behavior of the R `unique` function for floating point vectors. Now unique values are determined by the `y.precison` argument which defaults to multiplying values by $10^5$ before rounding. Details are in [this report](unique-float.html) by Shawn Garbett of the Vanderbilt Department of Biostatistics.
Version 6.8-0 also has an important new function for relative explained variation, `rexVar`.
`rms 6.7-0` appeared on CRAN 2023-05-08 and represents a major update. The most significant new feature is automatically computing all likelihood ratio (LR) $\chi^2$ chunk test statistics that can be inferred from the model design when the model is fitted using `lrm, orm, psm, cph, Glm`. I've been meaning to do this for more than 10 years because LR tests are more accurate than the default `anova.rms` Wald tests. LR tests do not suffer from the [Hauck-Donner effect](/rmsc/mle.html#sec-mle-hd) when a predictor has an infinite regression coefficient that drives the Wald $\chi^2$ to zero because the standard error blows up.
An example of a full LR `anova` is [here](/rmsc/psmcase.html#summarizing-the-fitted-model).
Also new is the implementation of LR tests when doing multiple imputation, using the method of [Chan and Meng](https://www3.stat.sinica.edu.tw/statistica/j32n3/j32n314/j32n314.html). This uses a new feature in `Hmisc:fit.mult.impute` where besides testing on individual completed datasets the log likelihood is computed from a stacked dataset of all completed datasets. Specifying `lrt=TRUE` to `fit.mult.impute` will take the necessary actions to get LR tests with `processMI` including setting argument `method` to `'stack'` which makes final regression coefficient estimates come from a single fit of a stacked dataset.
There are new `rms` functions or options relating to this:
* `LRupdate`: update LR test-related stats after `processMI` is run (including pseudo $R^2$ measures)
* `processMI.fit.mult.impute`: added processing of `anova` result from `fit.mult.impute(..., lrt=TRUE)`
* `prmiInfo`: print (or html) inputation parameters on the result of `processMI(..., 'anova')`
This new `rms` requires installing the latest `Hmisc` from CRAN.
## Documentation | [CRAN](http://cran.r-project.org/web/packages/rms) | [GitHub](https://github.com/harrelfe/rms) | [Online](https://www.rdocumentation.org/packages/rms)
* [Examples](/R/rms/examples.html) in an R
markdown/knitr html report
* [Vignette for general multiparameter transformations using the `gTrans` function](/R/examples/gTrans/gTrans.html)
* [Vignettes for Bayesian modeling with rmsb](/R/examples/blrm/blrm.html)
* [An Introduction to the Harrellverse](https://www.nicholas-ollberding.com/post/an-introduction-to-the-harrell-verse-predictive-modeling-using-the-hmisc-and-rms-packages) by Nicholas Ollberding
* [Linear Regression Case Study](https://thomaselove.github.io/432-notes/using-ols-from-the-rms-package-to-fit-linear-models.html) by Thomas Love
* [Markov models for longitudinal data](/stat/irreg.html), [here](/R/examples/simMarkovOrd/sim.html), [here](/proj/covid19/orchid.html), and [here](/proj/covid19/violet2.html)
* Many [test scripts](https://github.com/harrelfe/rms/tree/master/inst/tests)
* [Video](https://youtu.be/EoIB_Obddrk) demonstrating `survplotp` interactive survival curves
* [Online help with examples](https://www.rdocumentation.org/packages/rms)
* [Changelog](https://github.com/harrelfe/rms/commits/master) and [News](https://cran.r-project.org/web/packages/rms/NEWS)
* [Package overview](https://www.rdocumentation.org/packages/rms/topics/rmsOverview)
* [Manual](http://cran.r-project.org/web/packages/rms/rms.pdf)
* [Latest Linux source package](rms_current.tar.gz)
+ To install: Download and `sudo R CMD INSTALL rms-linux.tar.gz`
* [Latest binary packages for Linux, Windows, and Mac arm64](../bin/howto.html)
* Notes about [R^2 measures](/bib/r2.html)
## Evolution
`rms` is an R package that is a replacement for the `Design` package.
The package accompanies FE Harrell's book _[Regression Modeling
Strategies](/doc/rms)_. It began in 1991 as the S-Plus `Design` package.
## Bug Reports
Please use `Issues` on [GitHub](https://github.com/harrelfe/rms).