c(
person("Scott", "Chamberlain", role = "aut"),
person("Ben", "Tupper", role = "ctb"),
person("Roy", "Mendelssohn", role = c("cre", "ctb"),
email = "roy.mendelssohn@noaa.gov")
)
Scott Chamberlain [aut],
Ben Tupper [ctb],
Roy Mendelssohn [cre, ctb]

Extracts Environmental Data from 'ERDDAP' Web Services
person("Roy", "Mendelssohn", email = "roy.mendelssohn@noaa.gov", role = c("aut","cre"))
Contains three functions that access
environmental data from any 'ERDDAP' data web service. The rxtracto() function extracts
data along a trajectory for a given "radius" around the point. The
rxtracto_3D() function extracts data in a box. The rxtractogon() function
extracts data in a polygon. All of those three function use the 'rerddap' package
to extract the data, and should work with any 'ERDDAP' server.
There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
package to simplify the creation of maps of the data.
abind, dplyr, ggplot2, httr, methods, ncdf4, parsedate,
plotdap, readr, rerddap (>= 0.6.0), sp, stats,

abind, dplyr, ggplot2, httr, maps, methods, ncdf4, parsedate,
plotdap, readr, rerddap (>= 0.6.0), sf, sp, stats,

gganimate, knitr, mapdata, maptools, rgdal, rmarkdown

abind, dplyr, ggplot2, httr, maps, methods, ncdf4, parsedate,
plotdap (>= 0.0.5), readr, rerddap (>= 0.6.0), sf, sp, stats,

abind, dplyr, ggplot2, httr, maps, methods, ncdf4, parsedate,
plotdap (>= 0.0.5), readr, rerddap (>= 0.6.0), sf, sp, stats,
utils
cmocean, gganimate, knitr, mapdata, maptools, rgdal,
rmarkdown

c(
person("Roy", "Mendelssohn", email = "roy.mendelssohn@noaa.gov", role = c("aut","cre")),
person("Marie", "Auger-Methe", role = "ctb")
)
abind, dplyr, ggplot2, httr, maps, methods, ncdf4, parsedate,
plotdap (>= 0.0.5), readr, rerddap (>= 0.8.0), sf, sp, stats,
utils
Roy Mendelssohn [aut, cre],
Marie Auger-Methe [ctb]

James Browne (jbrowne6@jhu.edu) and Tyler Tomita (ttomita2@jhmi.edu)
Random Forester (RerF) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest - Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>.  Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal.  To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along.  This package, 'rerf', implements RerF which is similar to Forest-RC.  The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.
parallel, RcppZiggurat, utils, stats, dummies