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OrdFacReg  

Least Squares, Logistic, and Cox-Regression with Ordered Predictors
View on CRAN: Click here


Download and install OrdFacReg package within the R console
Install from CRAN:
install.packages("OrdFacReg")

Install from Github:
library("remotes")
install_github("cran/OrdFacReg")

Install by package version:
library("remotes")
install_version("OrdFacReg", "1.0.8")



Attach the package and use:
library("OrdFacReg")
Maintained by
Kaspar Rufibach
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2009-04-16
Latest Update: 2015-07-04
Description:
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
How to cite:
Kaspar Rufibach (2009). OrdFacReg: Least Squares, Logistic, and Cox-Regression with Ordered Predictors. R package version 1.0.8, https://cran.r-project.org/web/packages/OrdFacReg. Accessed 25 Oct. 2026.
Previous versions and publish date:
1.0.0 (2009-04-16 11:17), 1.0.1 (2009-09-28 15:36), 1.0.2 (2010-08-31 10:14), 1.0.3 (2011-12-01 09:00), 1.0.4 (2014-02-06 08:26), 1.0.5 (2014-09-06 20:52), 1.0.6 (2015-07-04 15:27), (2026-07-09 08:15)
Other packages that cited OrdFacReg R package
View OrdFacReg citation profile
Other R packages that OrdFacReg depends, imports, suggests or enhances
Complete documentation for OrdFacReg
Functions, R codes and Examples using the OrdFacReg R package
Some associated functions: internal . ordFacReg-package . ordFacReg . ordFacRegCox . prepareData . 
Some associated R codes: Full OrdFacReg package functions and examples
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