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 outlier_range_iqr <- c(range_IQR$1]- weightIQR x iqr, range_IQR(2)+ weightIQR x iqr) with range_IQR(1) the first quartile and range_IQR(2) the third quartile
`alpha_metrics_sfs()`	compute alpha metrics from list of spectral species distributions in the context of sequential feature selection
`alphabeta_chunk()`	apply alphabeta_window to a list of lists
`alphabeta_window()`	compute alpha and beta diversity metrics from pixel data corresponding to spectral species extracted from a window
`alphabeta_window_classif()`	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
`alphabeta_window_sdm()`	compute alpha and beta diversity metrics from pixel data corresponding to spectral species extracted from a window
`apply_bigRaster()`	This function performs big raster processing It requires user to write a function expecting vectors / matrices / dataframes corresponding to raster data read sequentially, a list of input files, a list of output files, and it is good to go.
`apply_continuum_removal()`	prepares data into list to apply continuum removal
`apply_kmeans()`	apply kmeans to information extracted from an image and corresponding to a window
`apply_spectral_species()`	compute spectral species from a raster
`apply_spectral_species_plot()`	apply biodivMapR to a set of plots identified by a field 'id' produced with preprocS2 function get_s2_tiling
`bbox_to_poly()`	converts a bbox to a polygon
`beta_metrics_sfs()`	compute beta metrics during sfs
`biodivMapR()`	computes diversity metrics from raster
`biodivMapR_chunk()`	apply biodivMapR (computes clusters + diversity metrics) to an image chunk
`biodivMapR_classif()`	computes diversity metrics from a classification raster containing information on a specific class for each pixel
`biodivMapR_opt_clusters()`	apply biodivMapR on a test set for different numbers of clusters
`biodivMapR_sample()`	sample pixels and plots to produce alpha and beta models, including clustering, dissimilarity & PCoA
`biodivMapR_sdm()`	computes diversity metrics from raster corresponding to SDM
`biodivMapR_sfs()`	performs SFS to identify combination of input variables maximizing a criterion
`biodivMapR_tiles()`	computes diversity metrics from raster
`biodivMapR_tiling()`	computes diversity metrics from a set of raster corresponding to tiles as produced with preprocS2
`bray_curtis_dissimilarity()`	Compute Bray-Curtis Dissimilarity
`bray_curtis_turnover()`	Compute Bray-Curtis Turnover
`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
`clean_tiff_list()`	exclude .aux.xml and .tfw files from a list of files including .tif extension
`compute_nn_from_ordination()`	compute the nearest neighbors among kernels
`compute_dissimilarity()`	compute dissimilarity matrix corresponding to two distinct matrices, each of them including a list of samples (rows) defined by their spectral species (columns) metrics available: bray, brayturn, simpson_diss, jaccard, jaccardturn, sorensen
`compute_mask_iqr()`	compute mask based on interquartile range criterion applied on input rasters
`compute_mask_iqr_tiles()`	computes a mask for a collection of tiles based on interquartile range filtering interquartile is computed for all data included in teh collection samples out of a range defined by quartile_1-weightIQR x (quartile_3 - quartile_1) and quartile_3+weightIQR x (quartile_3-quartile_1) are then considered as outliers
`continuumRemoval()`	Continuum Removal
`continuum_removal()`	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
`crfun()`	computes continuum removal for individual spectra
`crop_border_tile()`	crop border of a spatRaster
`define_grid()`	define a grid over a raster
`define_pixels_per_iter()`	defines the number of pixels per iteration
`biodivMapR_full()` `biodivMapR_full_tiles()`	Deprecated functions in biodivMapR
`exclude_spectral_domains()`	define water vapor bands based on spectral sampling of original image
`explore_cluster_range()`	explore performances for a range of cluster number
`explore_dissimilarity()`	explore disimilarity metrics for a range of number of clusters
`explore_kmeans()`	Compute kmeans from random subset of pixels extracted from an image and a list of values for k
`extract_from_rast_sfs()`	extract informaton from raster to prepare for SFS
`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
`fd_metrics_sfs()`	compute functional metrics during sfs
`file_path_sans_ext()`	Re-implementation of `file_path_sans_ext` in `tools`. This version can handle "." just before the file extenstion, unlike the original implementation. taken from https://github.com/cbig/zonator
`filenames_explore_cluster_range()`	checks if files produced from explore_cluster_range exist
`filenames_opt_clusters()`	checks if files produced from biodivMapR_opt_cluster exist
`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 functional diversity metrics from pixel data corresponding to features extracted from a window
`functional_window_list()`	apply functional_window to a list of lists
`get_hdr_name()`	get hdr name from image file name
`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_Shannon()`	computes shannon index from a distribution (faster than version implemented in vegan package)
`get_Simpson()`	computes Simpson index from a distribution
`get_alpha()`	computes alpha diversity metrics from ssd_mat corresponding to iterations
`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_beta_from_ssd()`	Computes beta diversity for a list of spectral species distributions
`get_cluster_list()`	get cluster list for biodivMapR_opt_clusters
`get_date()`	gets acquisition date from S2 image
`get_diversity_from_plots()`	computes diversity metrics from validation plots
`get_diversity_from_plots_cluster()`	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_input_chunk()`	get input chunk data from a list of rasters
`get_kmeans()`	computes k-means from nb_iter subsets defined from rast_sample
`get_normalized_ssd()`	get spectral species distribution for all clusters including those with 0 abundance
`get_normalized_ssd_list()`	get spectral species distribution from a list for all clusters including those with 0 abundance
`get_normalized_ssd_mat()`	get spectral species distribution for all clusters including those with 0 abundance
`get_plot_list()`	get a list of plots defined in a vector file defined as input
`get_plots_from_tiles()`	get samples for beta diversity mapping
`get_raster_diversity()`	Computes diversity metrics from raster data
`get_raster_diversity_mw()`	Computes diversity metrics from raster data based on moving window
`get_raster_diversity_tile()`	Computes diversity metrics from raster data
`get_raster_extent()`	gets raster extent
`get_samples_from_plots()`	extracts samples defined by a vector layer from a raster
`get_samples_from_tiles()`	get samples for alpha diversity mapping
`get_si_tiles_from_raster()`	computes spectral indices over a spatial extent from a raster
`get_spectralSpecies()`	compute spectral species from inputdata
`get_stats_from_tiles()`	get samples from tiles produced with preprocS2 function get_s2_tiling in order to produce stats
`get_sunlitwindows()`	eliminate windows with insufficient sunlit pixels
`get_valid_pixels()`	get valid pixels from a list of plots produced with preprocS2 function get_s2_tiling (notNA)
`get_valid_pixels_from_tiles()`	get valid pixels from list of tiles
`get_xy_samples()`	get xy of pixels to sample from raster
`harmonize_envi_hdr()`	Harmonize ENVI header file
`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
`jaccard_dissimilarity()`	Compute Jaccard Dissimilarity
`jaccard_turnover()`	Compute Jaccard Turnover
`kmeans_iter()`	computes kmeans for an iteration in biodivMapR
`kmeans_progressr()`	applies progression bar flr kmeans clustering
`maxRows_chunk()`	redefined chunks based on the max number of rows per chunk
`maximize_sfs_criterion()`	define variable maximizing a criterion
`meters_to_decdeg()`	Convert from meters to degrees correcting for global position borrowed from https://github.com/mlammens/occUncertain
`mnf()`	Function to perform MNF
`mosaic_from_vrt()`	mosaic tiles defined in vrt and compress if possible
`mosaic_tiles()`	produce a mosaic from a series of tiles
`nbRows_chunk()`	adjusts number of rows from chunks
`noise()`	gets noise
`num2char()`	converts numeric value to characters with specific lengths, adding 0 at beginning
`pca()`	performs PCA on matrix X
`pco()`	computes 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
`rast_sample_sfs()`	extract informaton from raster to prepare for SFS
`read_envi_header()`	Reads ENVI hdr file
`repmat()`	R equivalent of repmat (matlab)
`rm_invariant_bands()`	remove constant bands
`row_to_linear()`	get max index for each row and convert into linear index
`run_biodivMapR()`	apply biodivMapR to an individual set of rasters (one site)
`run_biodivMapR_plot()`	apply biodivMapR to a set of plots identified by a field 'id' produced with preprocS2 function get_s2_tiling
`sample_exact_raster()`	sample exact number of pixels from a raster
`sample_from_plots()`	get samples for alpha and beta diversity mapping
`sample_from_plots_alpha()`	get samples for alpha diversity mapping
`sample_from_plots_beta()`	get samples for beta diversity mapping
`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
`save_diversity_maps_mw()`	save diversity maps as raster data once moving window process done
`save_diversity_maps_tile()`	save diversity maps as raster data
`scatter_alphabeta()`	produces a figure summarizing alpha and beta diversity on scatterplots
`set_options_biodivMapR()`	set options
`simpson_dissimilarity()`	Compute Simpson Dissimilarity
`sorensen_dissimilarity()`	Compute Sorensen Dissimilarity
`spectral_species_full()`	computes diversity metrics from raster
`spectral_species_full_tiles()`	computes spectral species from raster
`spectralspecies_per_polygon()`	get spectral species corresponding to polygons in a SpatVector object
`spectralspecies_per_rast_sample()`	get spectral species corresponding to raster sample extracted with extract
`split_chunk()`	split chunk into subsets to prepare for parallel processing
`split_line()`	split information from a line
`update_mask_from_tiles()`	update mask based on IQR for a series of rasters produced with preprocS2 function get_s2_tiling
`weighted_coords_nn()`	Compute weighted coordinates of a spatial unit based on nearest neighbors used during PCoA
`which_alpha_beta()`	identifies which alpha/beta metrics should be used for next processes
`wrapperBig_PCA()`	this function aims at applying PCA on a raster or list of rasters in combination with the function apply_bigRaster
`write_envi_header()`	writes ENVI hdr file

Reference

All functions