Package index • biodivMapR

All functions
`ENVI_type2bytes()`	get information corresponding to data type defined in ENVI
`Get_FunctionalMetrics_From_Traits()`	Prepare for the computation of the functional diversity metrics
`IQR_outliers()`	This function computes interquartile range (IQR) criterion, which can be used as a criterion for outlier detection
`RW_bytes()`	identifies bytes where to read or write for each piece of an image in a given file
`RW_bytes_all()`	identifies bytes where to read and write for each piece of image and all files
`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
`Write_Big_Image()`	write an image which size is > 2**31-1
`Write_Image_NativeRes()`	write an image resulting from "window processing" at native spatial resolution (assuming square windows & origin at top left corner)
`ZipFile()`	Zips an image file
`apply_continuum_removal()`	prepares data to run multithreaded continuum removal
`apply_kmeans()`	Applies Kmeans clustering to PCA image and writes spectral species map
`build_image_from_list()`	rebuilds full image from list of subsets
`center_reduce()`	center and reduce data matrix based on known mean and SD
`change_resolution_HDR()`	change resolution in a HDR file
`check_data()`	Checks if the data to be processed has the format type expected
`check_update_mask_format()`	check if the format of the mask is as expected: integer, coded in Bytes, same dimensions as input image
`compute_ALPHA_FromPlot()`	compute alpha diversity from spectral species computed for a plot expecting a matrix of spectral species (n pixels x p repetitions)
`compute_BCdiss()`	compute the 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 ([[1]] and [[2]]) pcelim is the threshold for minimum contributin of a spctral species to be kept
`compute_BETA_FromPlots()`	compute beta diversity from spectral species computed for a plot expecting a matrix of spectral species (n pixels x p repetitions)
`compute_FD()`	compute functional diversity metrics for an array, given a specific window size
`compute_Functional_metrics()`	Map functional diversity metrics based on spectral species
`compute_NN_from_ordination()`	compute the nearest neighbors among kernels used in NMDS
`compute_SSD()`	compute spectral species distribution from original spectral species map
`compute_alpha_metrics()`	Computes alpha diversity metrics based on spectral species
`compute_beta_metrics()`	computes beta diversity metrics
`compute_spectral_species()`	this function reads PCA file and defines the spectral species for each pixel based on the set of cluster centroids defined for each iteration applies kmeans –> closest cluster corresponds to the "spectral species"
`compute_spectral_species_FieldPlots()`	compute spectral species for a subset of pixels provided in a list, each element corresponding to a polygon
`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
`convert_PCA_to_SSD()`	Convert PCA into SSD based on previous clustering
`create_hdr()`	create a hdr file for a raster
`create_mask_from_threshold()`	create a mask based on NDVI, Green reflectance and NIR reflectance NDVI (min) threshold eliminates non vegetated pixels Blue (max) threshold eliminates Clouds NIR (min) threshold eliminates shadows ! only valid if Optical data!!
`define_output_directory()`	define output directory and create it if necessary
`define_output_subdir()`	define output directory and subdirectory and create it if necessary
`define_pixels_per_iter()`	defines the number of pixels per iteration based on a trade-off between image size and sample size per iteration
`diversity_from_plots()`	gets alpha diversity indicators from plot
`exclude_spectral_domains()`	define Water Vapor bands based on spectral smapling of original image
`extract.big_raster()`	Extract bands of sparse pixels in image data cube
`extract_pixels()`	extracts sample points from binary image file
`extract_pixels_coordinates()`	Extracts pixels coordinates from raster intersecting a vector.
`extract_samples_from_image()`	extracts pixels from image based on their coordinates
`filter_PCA()`	perform filtering based on extreme values PCA identified through PCA
`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
`getBCdiss()`	Computes BC dissimilarity for a given matrix
`getFD()`	get functional diversity metrics from dataframe This function was inspired from FD package
`get_HDR_name()`	get hdr name from image file name, assuming it is BIL format
`get_SSpath()`	defines path for the spectral species raster file. Depends on preprocessing: - if standard dimensionality reduction (PCA, SPCA, MNF) was used, the corresponding directory is pointed - if no dimensionality reduction, or an alternative dimensionality reduction (spectral indices, tasseled cap... ) was used, user defined subdirectory is used - Diversity metrics can also be computed from a classification map
`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_metrics()`	Computes alpha diversity metrics from distribution
`get_byte_order()`	does the system work with little endians or big endians?
`get_gdal_info()`	Get GDAL info as a nested list
`get_image_bands()`	gets rank of spectral bands in an image
`get_random_subset_from_image()`	extract random subset of pixels from an image
`get_sunlit_pixels()`	Gets sunlit pixels from SpectralSpecies_Distribution_Sunlit
`ind2sub()`	convert image coordinates from index to X-Y
`ind2sub2()`	convert image coordinates from index to X-Y image coordinates are given as index = (ID.col-1) * total.lines + ID.row
`init_kmeans()`	computes k-means from nb_partitions subsets taken from dataPCA
`kmeans_progressr()`	applies results of ordination to full image based on nearest neighbors
`list_shp()`	Get list of shapefiles in a directory
`map_alpha_div()`	maps alpha diversity indicators based on prior selection of PCs
`map_beta_div()`	maps beta diversity indicator based on spectral species distribution
`map_functional_div()`	maps functional diversity indicators based on prior selection of PCs
`map_spectral_species()`	maps spectral species based on PCA file computed previously
`mean_filter()`	applies mean filter to an image
`minmax()`	this function performs rescaling and either defines min and max from each feature in a data set, or applies the transformation based on a previously defined min and max
`mnf()`	Function to perform MNF
`noise()`	this function
`ordination_to_NN()`	#' computes NMDS # #' @param MatBCdist BC dissimilarity matrix #' @param dimMDS numeric. number of dimensions of the NMDS # #' @return BetaNMDS_sel #' @importFrom future plan multisession sequential #' @importFrom future.apply future_lapply #' @importFrom ecodist nmds #' @importFrom utils find #' @export
`ordination_to_NN_list()`	computes nearest neighbors of a SSD subset with samples used for the adjustment of the ordination, then gets the coordinates of each window in the ordination space
`pca()`	Function to perform PCA on a matrix
`perform_PCA()`	Performs PCA for all images and create PCA file with either all or a selection of PCs
`perform_radiometric_filtering()`	Performs radiometric filtering based on three criteria: NDVI, NIR reflectance, Blue reflectance
`prepare_HDR_SSD()`	prepares Spectral species distribution (SSD) file and header
`prepare_HDR_Sunlit()`	prepare for writing sunlit proportion file
`print_error_message()`	prints an error message if problems occur
`raster2BIL()`	converts a raster into BIL format as expected by biodivMapR codes
`read_BIL_image_subset()`	reads subset of an ENVI BIL image
`read_ENVI_header()`	Reads ENVI hdr file
`read_bin_subset()`	reads a subset from a binary image
`read_image_bands()`	read specific image bands from image
`read_image_subset()`	reads subset of lines from an image
`repmat()`	R equivalent of repmat (matlab)
`revert_resolution_HDR()`	revert resolution in a HDR file
`rm_invariant_bands()`	remove constant bands
`select_PCA_components()`	Check if principal components are properly selected as expected by the method
`spectral_band_unit()`	Check spectral band units and convert from nanometer to micrometer or from micrometer to nanometer
`split_image()`	defines the number of pieces resulting from image split
`split_line()`	ENVI functions
`split_pixel_samples()`	splits a set of pixels to be sampled in an image based on number of lines, not number of samples
`sub2ind()`	convert image coordinates from X-Y to index
`update_shademask()`	updates an existing mask
`where_to_read()`	defines which byte should be read for each part of an image split in nbPieces
`where_to_read_kernel()`	defines which byte should be read for each part of an image split in nbPieces
`where_to_write_kernel()`	defines which byte should be written for each part of an image split in nbPieces
`write_ENVI_header()`	writes ENVI hdr file
`write_PCA_raster()`	writes an ENVI image corresponding to PCA
`write_StarsStack()`	This function writes a stars object into a raster file
`write_raster()`	Writes a matrix or an array into a ENVI BIL raster

Reference

All functions