c(person("James", "Browne", role = c("aut", "cre"),
email = "jbrowne6@jhu.edu"),
person("Tyler", "Tomita", role = "aut",
email = "ttomita2@jhmi.edu"),
person("Joshua", "Vogelstein", role = "ths",
email = "jovo@jhu.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.
James Browne [aut, cre],
Tyler Tomita [aut],
Joshua Vogelstein [ths]

c(
person("Jesse", "Patsolic", role = c("ctb", "cre"), email = "software@neurodata.io"),
person("Benjamin", "Falk", role = "ctb", email = "falk.ben@jhu.edu"),
person("Jaewon", "Chung", role = "ctb", email = "j1c@jhu.edu"),
person("James", "Browne", role = "aut", email = "jbrowne6@jhu.edu"),
person("Tyler", "Tomita", role = "aut", email = "ttomita2@jhmi.edu"),
person("Joshua", "Vogelstein", role = "ths", email = "jovo@jhu.edu")
)
R-RerF (aka Randomer Forest (RerF) or Random Projection
Forests) 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.
Jesse Patsolic [ctb, cre],
Benjamin Falk [ctb],
Jaewon Chung [ctb],
James Browne [aut],
Tyler Tomita [aut],
Joshua Vogelstein [ths]