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SESraster

Randomization of presence/absence species distribution raster data for calculating standardized effect sizes and testing null hypothesis. The randomization algorithms are based on classical algorithms for matrices (Gotelli 2000, doi:10.2307/177478) implemented for raster data.

Installation

To install the package, run:

install.packages("SESraster")

The development version can be installed from the Github repository:

require(devtools)
install_github("HemingNM/SESraster", build_vignettes = TRUE)

SESraster basics

Basic information about the package can be found below, at the package's webpage, or in the vignettes browseVignettes("SESraster")

  • Null model algorithms

vignette("null-models"): For an overview of the null model algorithms for species co-occurrence analysis summarized in (Gotelli 2000, doi:10.2307/177478).

  • Spatial null model algorithms in SESraster

vignette("spatial-null-models"): Get started with SESraster. See installation instructions and how the implemented null model algorithms work with spatial data.

  • Standardized effect sizes

vignette("SES"): For computing standardized effect sizes (SES) using the implemented null model algorithms.

Citation

  • If you use this R package, please cite in your publications:

Heming N. M., Mota F. M. M., Alves-Ferreira G. (2023). SESraster: Raster Randomization for Null Hypothesis Testing. R package version 0.7.0, https://CRAN.R-project.org/package=SESraster

  • For more information:
citation("SESraster")

Issues

If you have any question or find any bug, let us know through the topic "Issues".

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Version

Install

install.packages('SESraster')

Monthly Downloads

635

Version

0.7.0

License

GPL (>= 3)

Issues

Pull Requests

Stars

Forks

Maintainer

Neander Marcel Heming

Last Published

August 10th, 2023

Functions in SESraster (0.7.0)

bootspat_str

Spatially structured sample
.lyr.sample

Internal function to resample a vector according to the observed frequency
load_ext_data

Load SESraster external datasets
plot_alg_metrics

Plot performance of randomization algorithms
SESraster

Standardized effect sizes for SpatRaster objects
algorithm_metrics

Performance of randomization algorithms
bootspat_naive

Randomize a set of rasters according to the observed frequency.
.sample.not.NA

Internal function to sample vectors with non-NA values
fr2prob

Adjust probability of sampling based on frequency of occurrences.
fit.memory

Function to evaluate if the rasters generated in the function fit on RAM memory
bootspat_ff

Spatially structured fixed-fixed sample
.str.sample

Vectorized structured sample