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Implements species distribution modeling and ecological niche modeling, including: bias correction, spatial cross-validation, model evaluation, raster interpolation, biotic "velocity" (speed and direction of movement of a "mass" represented by a raster), interpolating across a time series of rasters, and use of spatially imprecise records. The heart of the package is a set of "training" functions which automatically optimize model complexity based number of available occurrences. These algorithms include MaxEnt, MaxNet, boosted regression trees/gradient boosting machines, generalized additive models, generalized linear models, natural splines, and random forests. To enhance interoperability with other modeling packages, no new classes are created. The package works with 'PROJ6' geodetic objects and coordinate reference systems.
Version: | 1.1.9 |
Depends: | R (≥ 4.0.0) |
Imports: | AICcmodavg, boot, data.table, doParallel, DT, foreach, gbm, graphics, ks, maxnet, methods, mgcv, omnibus, parallel, predicts, ranger, rJava, scales, sf, shiny, sp, statisfactory, stats, terra, utils |
Published: | 2024-11-03 |
DOI: | 10.32614/CRAN.package.enmSdmX |
Author: | Adam B. Smith [cre, aut] |
Maintainer: | Adam B. Smith <adam.smith at mobot.org> |
BugReports: | https://github.com/adamlilith/enmSdmX/issues |
License: | MIT + file LICENSE |
URL: | https://github.com/adamlilith/enmSdmX |
NeedsCompilation: | no |
Citation: | enmSdmX citation info |
Materials: | README NEWS |
CRAN checks: | enmSdmX results |
Reference manual: | enmSdmX.pdf |
Package source: | enmSdmX_1.1.9.tar.gz |
Windows binaries: | r-devel: enmSdmX_1.1.9.zip, r-release: enmSdmX_1.1.9.zip, r-oldrel: enmSdmX_1.1.9.zip |
macOS binaries: | r-release (arm64): enmSdmX_1.1.9.tgz, r-oldrel (arm64): enmSdmX_1.1.9.tgz, r-release (x86_64): enmSdmX_1.1.9.tgz, r-oldrel (x86_64): enmSdmX_1.1.9.tgz |
Old sources: | enmSdmX archive |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.