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jvcoords  

Principal Component Analysis (PCA) and Whitening
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("jvcoords", "1.0.3")



Attach the package and use:
library("jvcoords")
Maintained by
Jochen Voss
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-12-18
Latest Update: 2021-06-05
Description:
Provides functions to standardize and whiten data, and to perform Principal Component Analysis (PCA). The main advantage of this package over alternatives like prcomp() is, that jvcoords makes it easy to convert (additional) data between the original and the transformed coordinates. The package also provides a class coords, which can represent affine coordinate transformations. This class forms the basis of the transformations provided by the package, but can also be used independently. The implementation has been optimized to be of comparable speed (and sometimes even faster) than existing alternatives.
How to cite:
Jochen Voss (2018). jvcoords: Principal Component Analysis (PCA) and Whitening. R package version 1.0.3, https://cran.r-project.org/web/packages/jvcoords. Accessed 04 Jun. 2026.
Previous versions and publish date:
1.0.2 (2018-12-18 00:20)
Other packages that cited jvcoords R package
View jvcoords citation profile
Other R packages that jvcoords depends, imports, suggests or enhances
Complete documentation for jvcoords
Functions, R codes and Examples using the jvcoords R package
Some associated functions: PCA . coords . jvcoords-package . standardize . whiten . 
Some associated R codes: PCA.R . coords.R . standardize.R . whiten.R .  Full jvcoords package functions and examples
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