@article{halko2011,
	title = {An Algorithm for the Principal Component Analysis of Large Data Sets},
	copyright = {Copyright © 2011 Society for Industrial and Applied Mathematics},
	url = {https://epubs.siam.org/doi/abs/10.1137/100804139},
	doi = {10.1137/100804139},
	abstract = {Recently popularized randomized methods for principal component analysis (PCA) efficiently and reliably produce nearly optimal accuracy—even on parallel processors—unlike the classical (deterministic) alternatives. We adapt one of these randomized methods for use with data sets that are too large to be stored in random-access memory (RAM). (The traditional terminology is that our procedure works efficiently out-of-core.) We illustrate the performance of the algorithm via several numerical examples. For example, we report on the PCA of a data set stored on disk that is so large that less than a hundredth of it can fit in our computer's RAM.},
	language = {en},
	urldate = {2022-02-22},
	journal = {SIAM Journal on Scientific Computing},
	author = {Halko, Nathan and Martinsson, Per-Gunnar and Shkolnisky, Yoel and Tygert, Mark},
	month = {oct},
	year = {2011},
	note = {Publisher: Society for Industrial and Applied Mathematics},
}

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}

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}

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@inbook{legendrelegendre,
  author    = {Pierre Legendre and Louis Legendre},
  title     = {Numerical Ecology},
  publisher = {Elsevier},
  year      = {2012},
  isbn      = {0444-89249},
  edition   = {Third},
  pages     = {499}
}

@Article{Faith1987,
  author={Faith, Daniel P. and Minchin, Peter R. and Belbin, Lee},
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  abstract={The robustness of quantitative measures of compositional dissimilarity between sites was evaluated using extensive computer simulations of species' abundance patterns over one and two dimensional configurations of sample sites in ecological space. Robustness was equated with the strength over a range of models, of the linear and monotonic (rank-order) relationship between the compositional dissimilarities and the corresponding Euclidean distances between sites measured in the ecological space. The range of models reflected different assumptions about species' response curve shape, sampling pattern of sites, noise level of the data, species' interactions, trends in total site abundance, and beta diversity of gradients.},
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}

@Manual{Oksanen2018,
  title = {vegan: Community Ecology Package},
  author = {Jari Oksanen and F. Guillaume Blanchet and Michael Friendly and Roeland Kindt and Pierre Legendre and Dan McGlinn and Peter R. Minchin and R. B. O'Hara and Gavin L. Simpson and Peter Solymos and M. Henry H. Stevens and Eduard Szoecs and Helene Wagner},
  year = {2018},
  note = {R package version 2.5-3},
  url = {https://CRAN.R-project.org/package=vegan},
}
