Package index • biodivMapR

All functions
`IQR_SpatRaster()`	This function computes interquartile range (IQR) for a SpatRaster
`IQR_outliers()`	This function computes interquartile range (IQR) criterion, which can be used as a criterion for outlier detection
`RowToLinear()`	get max index for each row and convert into linear index
`WeightedCoordsNN()`	Compute the weighted coordinates of a spatial unit based on nearest neighbors used during PCoA
`alphabeta_window()`	compute alpha and beta diversity metrics from pixel data corresponding to spectral species extracted from a window
`alphabeta_window_list()`	apply alphabeta_window to a list of lists
`apply_continuum_removal()`	prepares data to run multithreaded continuum removal
`apply_kmeans()`	apply kmeans to information extracted from an image and corresponding to a window
`biodivMapR_OptClusters()`	explores performances of biodivMapR for different numbers of clusters
`biodivMapR_SFS()`	performs SFS to identify combination of input variables maximizing a criterion
`biodivMapR_chunk()`	apply biodivMapR (computes clusters + diversity metrics) to an image chunk
`biodivMapR_full()`	computes diversity metrics from raster
`center_reduce()`	center and reduce data matrix based on known mean and SD
`check_data()`	Checks if the data to be processed has the format type expected
`clean_NAsInf()`	cleans dataframe from NAs and Inf values
`compute_BCdiss()`	compute bray curtis dissimilarity matrix corresponding to a list of kernels (rows) defined by their spectral species (columns) SSDList is a list containing spectral species distribution for two sets of kernels pcelim is the threshold for minimum contributin of a spctral species to be kept
`compute_NN_from_ordination()`	compute the nearest neighbors among kernels
`compute_mask_IQR()`	compute mask based on interquartile range criterion applied on input rasters
`continuumRemoval()`	Computes continuum removal for matrix shaped data: more efficient than processing individual spectra the convex hull is based on the computation of the derivative between R at a given spectral band and R at the following bands
`define_pixels_per_iter()`	defines the number of pixels per iteration
`exclude_spectral_domains()`	define Water Vapor bands based on spectral sampling of original image
`explore_kmeans()`	Compute kmeans from random subset of pixels extracted from an image and a list of values for k
`extract_svc_from_rast()`	extract pixel information from a raster based on SpatVectorCollection
`extract_vect_from_rast()`	extract pixel information from a raster based on vector footprint
`filter_prior_CR()`	Filter data prior to continuum removal: values are expected to be real reflectance values between 0 and 10000 negative values may occur, so a +100 value is applied to avoid negative possibly remaining negative values are set to 0 constant spectra are eliminated
`functional_window()`	compute alpha and beta diversity metrics from pixel data corresponding to spectral species extracted from a window
`functional_window_list()`	apply functional_window to a list of lists
`get_BCdiss()`	Computes BC dissimilarity for a given matrix
`get_BCdiss_from_SSD()`	Computes BC dissimilarity for a list of spectral species distributions
`get_HDR_name()`	get hdr name from image file name, assuming it is BIL format
`get_Hill()`	computes hill number from a distribution The Hill numbers quantify biodiversity. The importance of the abundance distribution increases with increasing Hill order. For q=0, the Hill number is the richness, for q=1, it is the exponential Shannon entropy and for q=2, it is the inverse Simpson index. Note that the Hill order can also be a fraction, e.g. 0.5.
`get_SSD_full()`	get spectral species distribution for all clusters, even those with null abundance
`get_Shannon()`	computes shannon index from a distribution (faster than version implemented in vegan package)
`get_Simpson()`	computes Simpson index from a distribution (faster than version implemented in vegan package)
`get_alpha_from_SSD()`	computes alpha diversity metrics from SSD
`get_asset_path()`	This function gets path from an asset in the JSON file
`get_date()`	This function gets acquisition date from S2 image
`get_diversity_from_plots()`	computes diversity metrics from validation plots
`get_functional_diversity()`	get functional diversity metrics from dataframe This function was inspired from FD package
`get_image_bands()`	gets rank of spectral bands in an image
`get_kmeans()`	computes k-means from nbIter subsets taken from dataPCA
`get_raster_diversity()`	Computes diversity metrics from raster data
`get_raster_extent()`	gets raster extent
`get_spectralSpecies()`	compute spectral species from inputdata
`get_sunlitwindows()`	eliminate windows with insufficient sunlit pixels
`get_xy_samples()`	get xy of pixels to sample from raster
`init_PCoA()`	initialize PCoA for beta diversity mapping
`init_PCoA_samples()`	initialize PCoA for beta diversity mapping based on samples extracted from images
`init_kmeans()`	Compute kmeans from random subset of pixels extracted from an image
`init_kmeans_samples()`	Compute kmeans from random subset of pixels extracted from an image
`kmeans_iter()`	computes kmeans for an iteration in biodivMapR
`kmeans_progressr()`	applies results of ordination to full image based on nearest neighbors
`maxRows_chunk()`	redefined chunks based on the max number of rows per chunk
`mnf()`	Function to perform MNF
`nbRows_chunk()`	adjusts number of rows from chunks
`noise()`	this function
`pca()`	Function to perform PCA on a matrix
`pco()`	compute PCoA using the cmdscale function original source: package labdsv https://rdrr.io/cran/labdsv/src/R/pco.R
`perform_PCA()`	Performs PCA for all images and create PCA file with either all or a selection of PCs
`print_error_message()`	prints an error message if problems occur
`produce_win_ID()`	design a matrix with window ID based on an original raster and window size in pixels
`radiometric_filtering()`	Performs radiometric filtering based on three criteria: NDVI, NIR reflectance, Blue reflectance
`radiometricfilter_chunk()`	Performs radiometric filtering based on three criteria: NDVI, NIR reflectance, Blue reflectance
`randperm()`	performs random permutation for k samples among the vector defined by a original source: package labdsv https://rdrr.io/cran/labdsv/src/R/pco.R
`read_ENVI_header()`	Reads ENVI hdr file
`repmat()`	R equivalent of repmat (matlab)
`rm_invariant_bands()`	remove constant bands
`sample_exact_raster()`	sample exact number of pixels from a raster
`sample_from_raster()`	sample pixels or plots from raster data
`sample_from_raster_coords()`	sample set of pixels defined by row and col from raster data
`sample_plots_from_raster()`	sample pixels or plots from raster data
`sample_raster()`	sample pixels from raster data
`save_diversity_maps()`	save diversity maps as raster data
`scatter_alphabeta()`	produces a figure summarizing alpha and beta diversity on scatterplots
`select_PCA_components()`	Check if principal components are properly selected as expected by the method
`spectralspecies_per_polygon()`	get spectral species corresponding to polygons in a SpatVector object
`split_chunk()`	split chunk into subsets to prepare for parallel processing
`split_line()`	ENVI functions
`wrapperBig_PCA()`	this function aims at applying PCA on a raster or list of rasters in combination with the function apply_bigRaster

Reference

All functions