Package: NLMR 1.2.0

Jakub Nowosad

NLMR: Simulating Neutral Landscape Models

Provides neutral landscape models (<doi:10.1007/BF02275262>, <https://sci-hub.in/10.1007/bf02275262>). Neutral landscape models range from "hard" neutral models (completely random distributed), to "soft" neutral models (definable spatial characteristics) and generate landscape patterns that are independent of ecological processes. Thus, these patterns can be used as null models in landscape ecology. 'NLMR' combines a large number of algorithms from other published software for simulating neutral landscapes. The simulation results are obtained in a spatial data format (raster* objects from the 'raster' package) and can, therefore, be used in any sort of raster data operation that is performed with standard observation data.

Authors:Marco Sciaini [aut], Matthias Fritsch [aut], Maximilian Hesselbarth [aut], Craig Simpkins [aut], Cédric Scherer [aut], Sebastian Hanß [aut], Jakub Nowosad [aut, cre], Laura Graham [rev], Jeffrey Hollister [rev]

NLMR_1.2.0.tar.gz
NLMR_1.2.0.zip(r-4.7)NLMR_1.2.0.zip(r-4.6)NLMR_1.2.0.zip(r-4.5)
NLMR_1.2.0.tgz(r-4.6-x86_64)NLMR_1.2.0.tgz(r-4.6-arm64)NLMR_1.2.0.tgz(r-4.5-x86_64)NLMR_1.2.0.tgz(r-4.5-arm64)
NLMR_1.2.0.tar.gz(r-4.6-arm64)NLMR_1.2.0.tar.gz(r-4.6-x86_64)
NLMR_1.2.0.tgz(r-4.5-emscripten)
manual.pdf |manual.html
card.svg |card.png
NLMR/json (API)
NEWS

# Install 'NLMR' in R:
install.packages('NLMR', repos = c('https://packages.ropensci.org', 'https://cloud.r-project.org'))

Reviews:rOpenSci Software Review #188

Bug tracker:https://github.com/ropensci/nlmr/issues

Pkgdown/docs site:https://docs.ropensci.org

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

landscape-ecologyneutral-landscape-modelpeer-reviewedspatialcpp

7.82 score 68 stars 219 scripts 28 downloads 4 mentions 16 exports 43 dependencies

Last updated from:393db99722 (on main). Checks:14 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK204
linux-devel-x86_64OK213
pkgdown docsOK195
source / vignettesOK235
linux-release-arm64OK203
linux-release-x86_64OK219
macos-release-arm64OK153
macos-release-x86_64OK362
macos-oldrel-arm64OK119
macos-oldrel-x86_64OK323
windows-develOK150
windows-releaseOK158
windows-oldrelOK150
wasm-releaseOK160

Exports:nlm_curdsnlm_distancegradientnlm_edgegradientnlm_fbmnlm_gaussianfieldnlm_mosaicfieldnlm_mosaicgibbsnlm_mosaictessnlm_mpdnlm_neighnlm_percolationnlm_planargradientnlm_randomnlm_randomclusternlm_randomrectangularclusterutil_classify

Dependencies:backportscheckmateclassclassIntcliDBIdeldirdplyre1071fasterizegenericsglueKernSmoothlatticelifecyclemagrittrMASSMatrixpillarpkgconfigpolyclipproxyR6rasterRcppRcppArmadillorlangs2sfspspatstat.dataspatstat.geomspatstat.randomspatstat.univarspatstat.utilsterratibbletidyselectunitsutf8vctrswithrwk

Basic Usage of NLMR

Rendered fromgetstarted.Rmdusingknitr::rmarkdownon Apr 23 2026.

Last update: 2026-04-23
Started: 2018-01-10

Readme and manuals

Help Manual

Help pageTopics
nlm_curdsnlm_curds
nlm_distancegradientnlm_distancegradient
nlm_edgegradientnlm_edgegradient
nlm_fbmnlm_fbm
nlm_gaussianfieldnlm_gaussianfield
nlm_mosaicfieldnlm_mosaicfield
nlm_mosaicgibbsnlm_mosaicgibbs
nlm_mosaictessnlm_mosaictess nlm_polylands
nlm_mpdnlm_mpd
nlm_neighnlm_neigh
nlm_percolationnlm_percolation
nlm_planargradientnlm_planargradient
nlm_randomnlm_random
nlm_randomclusternlm_randomcluster
nlm_randomrectangularclusternlm_randomrectangularcluster
util_classifyutil_classify util_classify.RasterLayer