# Spatial Early Warning Signals of Ecosystem Degradation

## Documentation for package ‘spatialwarnings’ version 3.1.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

spatialwarnings-package | Early Spatial-Warnings of Ecosystem Degradation
arizona | Aerial views of vegetation from Arizona, USA
coarse_grain | Matrix coarse-graining
compute_indicator | Custom Spatial Early-Warning signals
convert_to_matrix | Convert an object to a matrix
create_indicator | Custom Spatial Early-Warning signals
custom_indicator | Custom Spatial Early-Warning signals
dda | Density-dependent aggregation model
dda.pars | Density-dependent aggregation model
display_matrix | Plot a matrix
dLSW | The Lifshitz-Slyozov-Wagner distribution
exp_fit | Distribution-fitting functions
extract_spectrum | Extract the r-spectrum from objects
extract_variogram | extract_variogram() method for variogram_sews objects
flowlength_sews | Flowlength connectivity indicator (uniform topography)
forestgap | A list of binary matrices and their associated parameters
forestgap.pars | A list of binary matrices and their associated parameters
generic_sews | Generic Spatial Early-Warning signals
indicator_plrange | Power-law range indicator
indicator_psdtype | Change in patch-size distributions types
indictest | Significance-assessment of spatial early-warning signals
kbdm_sews | Indicator based on Kolmogorov Complexity
label | Labelling of unique patches and detection of percolation.
lnorm_fit | Distribution-fitting functions
LSW_fit | The Lifshitz-Slyozov-Wagner distribution
lsw_sews | Indicators based on the LSW distribution
pair_counts | Clustering of pairs
patchdistr_sews | Early-warning signals based on patch size distributions
patchdistr_sews_plot | Early-warning signals based on patch size distributions
patchdistr_sews_predict | predict method for patchdistr_sews objects
patchsizes | Get patch sizes.
percolation | Labelling of unique patches and detection of percolation.
plot.patchdistr_sews | Early-warning signals based on patch size distributions
plot.simple_sews_list | Spatial early-warning signals: display of trends
plot.simple_sews_test | Spatial early-warning signals: display of trends
plot.variogram_sews | Early-warning signals based on variograms
plot.variogram_sews_test | Early-warning signals based on variograms
plot_distr | Early-warning signals based on patch size distributions
plot_spectrum | Display the r-spectrum of a 'spectral_sews' object
plot_variogram | Early-warning signals based on variograms
plot_variogram.variogram_sews | Early-warning signals based on variograms
plot_variogram.variogram_sews_test | Early-warning signals based on variograms
pLSW | The Lifshitz-Slyozov-Wagner distribution
pl_fit | Distribution-fitting functions
predict.patchdistr_sews_single | predict method for patchdistr_sews objects
predict.variogram_sews_list | predict() method for variogram_sews objects
raw_cg_moran | Moran's Index at lag of 1
raw_cg_skewness | Skewness indicator
raw_cg_variance | Spatial variance indicator
raw_clustering | Clustering of pairs
raw_flowlength_uniform | Flow length (uniform slope)
raw_kbdm | Kolmogorov complexity of a matrix
raw_lsw_aicw | Indicators based on the LSW distribution
raw_moran | Spatial correlation at lag 1
raw_patch_radii_skewness | Indicators based on the LSW distribution
raw_plrange | Power-law range indicator
raw_sdr | Spectral Density Ratio (SDR) indicator
raw_structvar | Structural variance
raw_variogram_metrics | Variogram parameters
rspectrum | r-spectrum
serengeti | Serengeti dataset
serengeti.rain | Serengeti dataset
simple_sews | 'simple_sews' objects
simple_sews_object | 'simple_sews' objects
spatialwarnings | Early Spatial-Warnings of Ecosystem Degradation
spectral_sews | Spectrum-based spatial early-warning signals.
tpl_fit | Distribution-fitting functions
variogram_sews | Early-Warning signals based on variograms (EXPERIMENTAL)
variogram_sews_plot | Early-warning signals based on variograms
variogram_sews_predict | predict() method for variogram_sews objects
xmin_estim | Estimate the minimum patch size of a power-law distribution