Rdistance: Distance-Sampling Analyses for Density and Abundance Estimation

Distance-sampling analyses estimate density and abundance of organisms in ecology when detection probability declines with increasing distance from the observers. Distance-sampling is popular in most branches of ecology and especially when organisms are observed from aerial platforms (e.g., airplane or drone), surface vessels (e.g., boat or truck), or along walking transects. Rdistance analyzes data collected on both point and line transects, estimates overall (study area) and site-level (transect or point) density, and allows users to specify regression-like formula (similar to lm or glm) for covariates. A large suite of classical, parametric detection functions are included along with some uncommon parametric functions (e.g., Gamma, negative exponential) and non-parametric smoothed distance functions. Custom (user-defined) detection functions can be implemented (see vignette). Measurement unit integrity is enforced with internal unit conversion when necessary. The help files and vignettes have been vetted by multiple authors and tested in workshop settings.

Package details

AuthorTrent McDonald [cre, aut], Jason Carlisle [aut], Aidan McDonald [aut] (point transect methods), Ryan Nielson [ctb] (smoothed likelihood), Ben Augustine [ctb] (maximization method), James Griswald [ctb] (maximization method), Patrick McKann [ctb] (maximization method), Lacey Jeroue [ctb] (vignettes), Hoffman Abigail [ctb] (vignettes), Kleinsausser Michael [ctb] (vignettes), Joel Reynolds [ctb] (Gamma likelihood), Pham Quang [ctb] (Gamma likelihood), Earl Becker [ctb] (Gamma likelihood), Aaron Christ [ctb] (Gamma likelihood), Brook Russelland [ctb] (Gamma likelihood), Stefan Emmons [ctb] (Automated tests), Will McDonald [ctb] (Automated tests), Reid Olson [ctb] (Automated tests and bug fixes)
MaintainerTrent McDonald <trent@mcdonalddatasciences.com>
LicenseGNU General Public License
URL https://github.com/tmcd82070/Rdistance/wiki
Package repositoryView on CRAN
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Rdistance documentation built on July 9, 2023, 6:46 p.m.