The R Journal: article published in 2017, volume 9:2

ider: Intrinsic Dimension Estimation with R
Hideitsu Hino , The R Journal (2017) 9:2, pages 329-341.

Abstract In many data analyses, the dimensionality of the observed data is high while its intrinsic dimension remains quite low. Estimating the intrinsic dimension of an observed dataset is an essential preliminary step for dimensionality reduction, manifold learning, and visualization. This paper introduces an R package, named ider, that implements eight intrinsic dimension estimation methods, including a recently proposed method based on a second-order expansion of a probability mass function and a generalized linear model. The usage of each function in the package is explained with datasets generated using a function that is also included in the package.

Received: 2017-04-27; online 2017-10-31, supplementary material, (950 B)
CRAN packages: ider, ider, ider, fractal, nonlinearTseries, tseriesChaos, fractal, nonlinearTseries, tseriesChaos, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, ider, Rcpp
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, HighPerformanceComputing, NumericalMathematics

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2017-054,
  author = {Hideitsu Hino},
  title = {{ider: Intrinsic Dimension Estimation with R}},
  year = {2017},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2017-054},
  url = {https://doi.org/10.32614/RJ-2017-054},
  pages = {329--341},
  volume = {9},
  number = {2}
}