Other packages > Find by keyword >

optiscale  

Optimal Scaling
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("optiscale", "1.2.3")



Attach the package and use:
library("optiscale")
Maintained by
Dave Armstrong
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2014-08-01
Latest Update: 2021-02-03
Description:
Optimal scaling of a data vector, relative to a set of targets, is obtained through a least-squares transformation subject to appropriate measurement constraints. The targets are usually predicted values from a statistical model. If the data are nominal level, then the transformation must be identity-preserving. If the data are ordinal level, then the transformation must be monotonic. If the data are discrete, then tied data values must remain tied in the optimal transformation. If the data are continuous, then tied data values can be untied in the optimal transformation.
How to cite:
Dave Armstrong (2014). optiscale: Optimal Scaling. R package version 1.2.3, https://cran.r-project.org/web/packages/optiscale. Accessed 22 Dec. 2024.
Previous versions and publish date:
1.1 (2014-08-01 07:55), 1.2.2 (2021-02-03 06:40), 1.2 (2020-02-28 08:20)
Other packages that cited optiscale R package
View optiscale citation profile
Other R packages that optiscale depends, imports, suggests or enhances
Complete documentation for optiscale
Functions, R codes and Examples using the optiscale R package
Some associated functions: elec92 . methods . opscale . optiscale-package . os.plot . shepard . stress . 
Some associated R codes: opscalev16.R .  Full optiscale package functions and examples
Downloads during the last 30 days
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

dmlalg  
Double Machine Learning Algorithms
Implementation of double machine learning (DML) algorithms in R, based on Emmenegger and Buehlmann ...
Download / Learn more Package Citations See dependency  
composits  
Compositional, Multivariate and Univariate Time Series Outlier Ensemble
A compositional multivariate and univariate time series outlier ensemble.It uses the four R packages ...
Download / Learn more Package Citations See dependency  
tropAlgebra  
Tropical Algebraic Functions
It includes functions like tropical addition, tropical multiplication for vectors and matrices. In t ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
Rfast2  
A Collection of Efficient and Extremely Fast R Functions II
A collection of fast statistical and utility functions for data analysis. Functions for regression, ...
Download / Learn more Package Citations See dependency  
wordspace  
Distributional Semantic Models in R
An interactive laboratory for research on distributional semantic models ('DSM', see < ...
Download / Learn more Package Citations See dependency  

23,394

R Packages

201,798

Dependencies

63,416

Author Associations

23,395

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA