Package: spatialwarnings 3.1.2

Alexandre Genin

spatialwarnings: Spatial Early Warning Signals of Ecosystem Degradation

Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).

Authors:Alain Danet [aut], Alexandre Genin [aut, cre], Vishwesha Guttal [aut], Sonia Kefi [aut], Sabiha Majumder [aut], Sumithra Sankaran [aut], Florian Schneider [aut]

spatialwarnings_3.1.2.tar.gz
spatialwarnings_3.1.2.zip(r-4.7-x86_64)spatialwarnings_3.1.2.zip(r-4.7-arm64)
spatialwarnings_3.1.2.tar.gz(r-4.7-arm64)spatialwarnings_3.1.2.tar.gz(r-4.7-x86_64)spatialwarnings_3.1.2.tar.gz(r-4.6-arm64)spatialwarnings_3.1.2.tar.gz(r-4.6-x86_64)
spatialwarnings_3.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
spatialwarnings/json (API)

# Install 'spatialwarnings' in R:
install.packages('spatialwarnings', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/spatial-ews/spatialwarnings/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:
  • arizona - Aerial views of vegetation from Arizona, USA
  • dda - Density-dependent aggregation model
  • dda.pars - Density-dependent aggregation model
  • forestgap - A list of binary matrices and their associated parameters
  • forestgap.pars - A list of binary matrices and their associated parameters
  • serengeti - Serengeti dataset
  • serengeti.rain - Serengeti dataset

On CRAN:

Conda:

cpp

2.00 score 1 stars 50 scripts 309 downloads 47 exports 32 dependencies

Last updated from:b5fb0554b9. Checks:8 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK190
linux-devel-x86_64OK233
source / vignettesOK257
linux-release-arm64OK187
linux-release-x86_64OK205
windows-devel-arm64OK441
windows-devel-x86_64OK212
wasm-releaseOK187

Exports:coarse_graincompute_indicatorconvert_to_matrixcreate_indicatordisplay_matrixdisplay_size_infodLSWexp_fitextract_spectrumextract_variogramflowlength_sewsgeneric_sewsindicator_plrangeindicator_psdtypeindictestkbdm_sewslabellnorm_fitLSW_fitlsw_sewspair_countspatchdistr_sewspatchsizespercolationpl_fitplot_distrplot_spectrumplot_variogrampLSWraw_cg_moranraw_cg_skewnessraw_cg_varianceraw_clusteringraw_flowlength_uniformraw_kbdmraw_lsw_aicwraw_moranraw_patch_radii_skewnessraw_plrangeraw_sdrraw_structvarraw_variogram_metricsrspectrumspectral_sewstpl_fitvariogram_sewsxmin_estim

Dependencies:clicodetoolscpp11digestfarverfuturefuture.applyggplot2globalsgluegslgtableisobandlabelinglatticelifecyclelistenvMASSnlmeparallellyplyrR6RColorBrewerRcppRcppArmadillorlangS7scalessegmentedvctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Aerial views of vegetation from Arizona, USAarizona
Matrix coarse-grainingcoarse_grain
Convert an object to a matrixconvert_to_matrix
Custom Spatial Early-Warning signalscompute_indicator create_indicator custom_indicator
Density-dependent aggregation modeldda dda.pars
Plot a matrixdisplay_matrix
Display size information of a sews_result objectdisplay_size_info display_size_info.list display_size_info.sews_result_list display_size_info.sews_result_single
The Lifshitz-Slyozov-Wagner distributiondLSW LSW_fit pLSW
Extract the r-spectrum from objectsextract_spectrum
extract_variogram() method for variogram_sews objectsextract_variogram
Flowlength connectivity indicator (uniform topography)flowlength_sews
A list of binary matrices and their associated parametersforestgap forestgap.pars
Generic Spatial Early-Warning signalsgeneric_sews
Power-law range indicatorindicator_plrange
Change in patch-size distributions typesindicator_psdtype
Significance-assessment of spatial early-warning signalsindictest
Indicator based on Kolmogorov Complexitykbdm_sews
Labelling of unique patches and detection of percolation.label percolation
Indicators based on the LSW distributionlsw_sews raw_lsw_aicw raw_patch_radii_skewness
Early-warning signals based on patch size distributionspatchdistr_sews
Early-warning signals based on patch size distributionspatchdistr_sews_plot plot.patchdistr_sews plot_distr
predict method for patchdistr_sews objectspatchdistr_sews_predict predict.patchdistr_sews_single
Get patch sizes.patchsizes
Distribution-fitting functionsexp_fit lnorm_fit pl_fit tpl_fit
Display the r-spectrum of a 'spectral_sews' objectplot_spectrum
Spatial early-warning signals: display of trendsplot.simple_sews_list plot.simple_sews_test
Moran's Index at lag of 1raw_cg_moran
Skewness indicatorraw_cg_skewness
Spatial variance indicatorraw_cg_variance
Clustering of pairspair_counts raw_clustering
Flow length (uniform slope)raw_flowlength_uniform
Kolmogorov complexity of a matrixraw_kbdm
Spatial correlation at lag 1raw_moran
Power-law range indicatorraw_plrange
Spectral Density Ratio (SDR) indicatorraw_sdr
Structural varianceraw_structvar
Variogram parametersraw_variogram_metrics
r-spectrumrspectrum
Serengeti datasetserengeti serengeti.rain
'simple_sews' objectssimple_sews simple_sews_object
Spectrum-based spatial early-warning signals.spectral_sews
Early-Warning signals based on variograms (EXPERIMENTAL)variogram_sews
Early-warning signals based on variogramsplot.variogram_sews plot.variogram_sews_test plot_variogram plot_variogram.variogram_sews plot_variogram.variogram_sews_test variogram_sews_plot
predict() method for variogram_sews objectspredict.variogram_sews_list variogram_sews_predict
Estimate the minimum patch size of a power-law distributionxmin_estim