Distance-sampling is a popular method for estimating density and abundance of organisms in ecology. Rdistance contains routines that assist with analysis of distance-sampling data collected on point or line transects. Distance models are specified using regression-like formula (similar to lm, glm, etc.). Abundance routines perform automated bootstrapping and automated detection-function selection. Overall (study area) and site-level (transect or point) abundance estimates are available. 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 are easily implemented (see vignette). The help files and vignettes have been vetted by multiple authors and tested in workshop settings.
|Author||Trent 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)|
|Maintainer||Trent McDonald <[email protected]>|
|License||GNU General Public License|
|Package repository||View on CRAN|
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