Package: clhs 0.9.2
clhs: Conditioned Latin Hypercube Sampling
Conditioned Latin hypercube sampling, as published by Minasny and McBratney (2006) <doi:10.1016/j.cageo.2005.12.009>. This method proposes to stratify sampling in presence of ancillary data. An extension of this method, which propose to associate a cost to each individual and take it into account during the optimisation process, is also proposed (Roudier et al., 2012, <doi:10.1201/b12728>).
Authors:
clhs_0.9.2.tar.gz
clhs_0.9.2.zip(r-4.7)clhs_0.9.2.zip(r-4.6)clhs_0.9.2.zip(r-4.5)
clhs_0.9.2.tgz(r-4.6-x86_64)clhs_0.9.2.tgz(r-4.6-arm64)clhs_0.9.2.tgz(r-4.5-x86_64)clhs_0.9.2.tgz(r-4.5-arm64)
clhs_0.9.2.tar.gz(r-4.7-arm64)clhs_0.9.2.tar.gz(r-4.7-x86_64)clhs_0.9.2.tar.gz(r-4.6-arm64)clhs_0.9.2.tar.gz(r-4.6-x86_64)
clhs_0.9.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
clhs/json (API)
NEWS
| # Install 'clhs' in R: |
| install.packages('clhs', repos = c('https://pierreroudier.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pierreroudier/clhs/issues
Last updated from:d49649a3b9. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 255 | ||
| linux-devel-x86_64 | OK | 276 | ||
| source / vignettes | OK | 285 | ||
| linux-release-arm64 | OK | 252 | ||
| linux-release-x86_64 | OK | 281 | ||
| macos-release-arm64 | OK | 188 | ||
| macos-release-x86_64 | OK | 526 | ||
| macos-oldrel-arm64 | OK | 147 | ||
| macos-oldrel-x86_64 | OK | 384 | ||
| windows-devel | OK | 272 | ||
| windows-release | OK | 264 | ||
| windows-oldrel | OK | 277 | ||
| wasm-release | OK | 137 |
Exports:clhssimilarity_buffer
Dependencies:classclassIntcliclustercpp11DBIe1071farverggplot2gluegtableisobandKernSmoothlabelinglatticelifecyclemagrittrMASSplyrproxyR6rasterRColorBrewerRcppRcppArmadilloreshape2rlangs2S7scalessfspstringistringrterraunitsvctrsviridisLitewithrwk
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Conditioned Latin Hypercube Sampling | clhs-package |
| Conditioned Latin Hypercube Sampling | clhs clhs.data.frame clhs.Raster clhs.sf clhs.SpatialPointsDataFrame |
| Conditioned Latin Hypercube Sampling result | cLHS_result |
| This is the internal Cpp function used to run the metropolis hasting algorithm if use.cpp = T. In general, it shouldn't be used as a stand alone function, because some preprocessing is done in R | CppLHS |
| Plot cLHS results | plot.cLHS_result |
| Gower similarity analysis | similarity_buffer |
