crsmeta (>= 0.3.0), dplyr, gridBase, magrittr, palr, png,
polyclip, raster, reproj (>= 0.4.2), rgl, rlang, RTriangle,
scales, silicate (>= 0.4.0), sp, terrainmeshr, tibble, unjoin,
utils, viridis,
covr, ggplot2, graticule, knitr, purrr, rmarkdown, sfheaders,
testthat (>= 2.1.0), trip, gibble

crsmeta (>= 0.3.0), dplyr, gridBase, magrittr, palr, png,
polyclip, raster, reproj (>= 0.4.2), rgl, rlang, RTriangle,
scales, silicate (>= 0.6.1), sp, terrainmeshr, tibble, unjoin,
utils, viridis, colourvalues

Tools for 'ROMS' the Regional Ocean Modeling System
person("Michael D.","Sumner", role = c("aut", "cre"), email = "mdsumner@gmail.com")
Helper functions for working with Regional Ocean Modeling System 'ROMS' output. See
<https://www.myroms.org/> for more information about 'ROMS'.

Inferring Dominance Hierarchies and Estimating Uncertainty
Damien R. Farine and Alfredo Sanchez-Tojar
Provides: (1) Tools to infer dominance hierarchies based on calculating Elo scores, but with custom functions to improve estimates in animals with relatively stable dominance ranks. (2) Tools to plot the shape of the dominance hierarchy and estimate the uncertainty of a given data set.

Simple animated versions of basic R plots, using the 'animation'
package. Includes animated versions of plot, barplot, persp, contour,
filled.contour, hist, curve, points, lines, text, symbols, segments, and
arrows.

maps, knitr, mapdata, testthat, rmarkdown

Extended Kalman Filters for Animal Movement
Synthetic generation of 1-D and 2-D correlated random walks (CRWs) for animal movement with behavioral switching, and particle filter estimation of movement parameters from observed trajectories using Extended Kalman Filter (EKF) model. See Ackerman (2018) <https://digital.library.temple.edu/digital/collection/p245801coll10/id/499150>.
MCMCpack, ellipse, mvtnorm, deldir, colorspace, Matrix, MASS,
png, grDevices, bezier, HDInterval, plyr, stats, methods,
utils, ggplot2, rgdal

Animal track reconstruction for high frequency 2-dimensional
(2D) or 3-dimensional (3D) movement data.
2D and 3D animal tracking data can be used to reconstruct tracks through time/space with correction based on known positions. 3D visualization of animal position and attitude.