High performance algorithms for manipulation of terrestrial 'LiDAR' (but not only) point clouds for use in research and forest monitoring applications, being fully compatible with the 'LAS' infrastructure of 'lidR'. For in depth descriptions of stem denoising and segmentation algorithms refer to Conto et al. (2017) <doi:10.1016/j.compag.2017.10.019>.
| Version: | 2.0.2 |
| Depends: | R (≥ 3.3.0), data.table (≥ 1.12.0), magrittr (≥ 1.5), lidR (≥ 3.0.0) |
| Imports: | rgl, raster, sp, deldir, dismo, nabor, benchmarkme, rlas, glue, mathjaxr |
| LinkingTo: | RcppArmadillo, Rcpp, BH, RcppEigen |
| Published: | 2020-08-26 |
| Author: | Tiago de Conto [aut, cre] |
| Maintainer: | Tiago de Conto <tdc.florestal at gmail.com> |
| License: | GPL-3 |
| URL: | https://github.com/tiagodc/TreeLS |
| NeedsCompilation: | yes |
| Materials: | README |
| CRAN checks: | TreeLS results |
| Reference manual: | TreeLS.pdf |
| Package source: | TreeLS_2.0.2.tar.gz |
| Windows binaries: | r-devel: TreeLS_2.0.2.zip, r-release: TreeLS_2.0.2.zip, r-oldrel: TreeLS_2.0.2.zip |
| macOS binaries: | r-release: TreeLS_2.0.2.tgz, r-oldrel: not available |
| Old sources: | TreeLS archive |
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