person("Ethan", "Ancell", email = "ethanancell@gmail.com", role = c("aut", "cre"))
A modified version of the classification and regression tree (CART)
algorithm for modelling spatial data that features coordinate information.
Coordinate information can be used to evaluate measures of spatial
autocorrelation and spatial compactness during the splitting phase of the
tree, leading to better predictions and more physically realistic predictions
on these types of datasets. These methods are described in Ancell and Bean (2021)
<arXiv:2101.08258>.

c(
person("Barret", "Schloerke", email = "schloerke@gmail.com", role = c("aut")),
person("Ryan", "Hafen", email = "rhafen@gmail.com", role = c("ths", "cre"))
)
Automatically calculates cognostic groups for plot objects and list column plot objects.  Results are returned in a nested data frame.
dplyr, checkmate, MASS, broom, moments, diptest, mclust,
ggplot2, tibble, utils, hexbin, progress
'autocog.R' 'known_cog_groups.R' 'add_cog_group.R'
'field_info.R' 'add_cog_group_.R' 'known_layer_cogs.R'
'add_layer_cogs.R' 'add_layer_cogs_.R' 'cog_desc.R'
'cog_spec.R' 'layer_count.R' 'layer_info.R' 'of_type.R'
'plot_class.R' 'plot_cogs.R'
Barret Schloerke [aut],
Ryan Hafen [ths, cre]

An implementation of sparse autoencoder for automatic learning
of representative features from unlabeled data.
Eugene Dubossarsky (project leader, chief designer), Yuriy Tyshetskiy (design, implementation, testing)
The package implements the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.

Sparse Autoencoder for Automatic Learning of Representative
Features from Unlabeled Data
Implementation of the sparse autoencoder in R environment, following the notes of Andrew Ng (http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/sparseAutoencoder.pdf). The features learned by the hidden layer of the autoencoder (through unsupervised learning of unlabeled data) can be used in constructing deep belief neural networks.