The R Journal: article published in 2014, volume 6:2

Prinsimp
Jonathan Zhang, Nancy Heckman, Davor Cubranic, Joel G. Kingsolver, Travis Gaydos and J.S. Marron , The R Journal (2014) 6:2, pages 27-42.

Abstract Principal Components Analysis (PCA) is a common way to study the sources of variation in a high-dimensional data set. Typically, the leading principal components are used to understand the variation in the data or to reduce the dimension of the data for subsequent analysis. The remaining principal components are ignored since they explain little of the variation in the data. However, the space spanned by the low variation principal components may contain interesting structure, structure that PCA cannot find. Prinsimp is an R package that looks for interesting structure of low variability. “Interesting” is defined in terms of a simplicity measure. Looking for interpretable structure in a low variability space has particular importance in evolutionary biology, where such structure can signify the existence of a genetic constraint.

Received: 2013-11-04; online 2014-09-28
CRAN packages: prinsimp

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2014-022,
  author = {Jonathan Zhang and Nancy Heckman and Davor Cubranic and Joel
          G. Kingsolver and Travis Gaydos and J.S. Marron},
  title = {{Prinsimp}},
  year = {2014},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2014-022},
  url = {https://doi.org/10.32614/RJ-2014-022},
  pages = {27--42},
  volume = {6},
  number = {2}
}