Package: lacunaritycovariance 1.1-7

lacunaritycovariance: Gliding Box Lacunarity and Other Metrics for 2D Random Closed Sets

Functions for estimating the gliding box lacunarity (GBL), covariance, and pair-correlation of a random closed set (RACS) in 2D from a binary coverage map (e.g. presence-absence land cover maps). Contains a number of newly-developed covariance-based estimators of GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and balanced estimators, proposed by Picka (2000) <http://www.jstor.org/stable/1428408>, for covariance, centred covariance, and pair-correlation. Also contains methods for estimating contagion-like properties of RACS and simulating 2D Boolean models. Binary coverage maps are usually represented as raster images with pixel values of TRUE, FALSE or NA, with NA representing unobserved pixels. A demo for extracting such a binary map from a geospatial data format is provided. Binary maps may also be represented using polygonal sets as the foreground, however for most computations such maps are converted into raster images. The package is based on research conducted during the author's PhD studies.

Authors:Kassel Liam Hingee

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Bug tracker:https://github.com/kasselhingee/lacunaritycovariance/issues

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43 exports 2 stars 1.03 score 22 dependencies 3 scripts 371 downloads

Last updated 11 months agofrom:12c59da113. Checks:OK: 5 NOTE: 2. Indexed: yes.

Exports:adjacencybddcovarbddcovar.isobddcoverageprobbdddiscrbddlambdabdrcovarbdrcoverageprobbpto.covarbpto.coverageprobbpto.germintensitycencovariancecencovariance.cvchatcontagdiscstatecontagpixelgridcovar.grainlibcoveragefraccoverageprobcpcppickagblgbl.cvchatgblcgblccgblcc.inputcovargblempgblggbltradinnerprod.imisbinarymapmeanarea.grainlibpaircorrpaircorr.cvchatplacegrainsfromlibplugincvcracscovarianceracscovariance.cvchatrbddrbdrrblndrbptosecondorderpropssummary.imlist

Dependencies:abinddeldirgoftestlatticeMatrixmgcvnlmepolyclipRcppRcppRollrpartspatstatspatstat.dataspatstat.explorespatstat.geomspatstat.linnetspatstat.modelspatstat.randomspatstat.sparsespatstat.univarspatstat.utilstensor