DESCRIPTION

Package: deepspat
Type: Package
Title: Deep Compositional Spatial Models
Date: 20 October 2023
Version: 0.2.0
Author: Andrew Zammit-Mangion and Quan Vu
Maintainer: Andrew Zammit-Mangion <andrewzm@gmail.com>
Description: Deep compositional spatial models are standard spatial covariance
             models coupled with an injective warping function of the spatial 
             domain. The warping function is constructed through a composition 
             of multiple elemental injective functions in a deep-learning 
             framework. The package implements two cases for the univariate setting; first,
	     when these warping functions are known up to some weights that
	     need to be estimated, and, second, when the weights in each layer are random.
	     In the multivariate setting only the former case is available.
	     Estimation and inference is done using TensorFlow, which makes use of 
             graphics processing units. 
License: Apache License 2.0
Imports: 
    data.table,
    dplyr,
    Matrix,
    methods,
    reticulate,
    tensorflow
SystemRequirements: TensorFlow (https://www.tensorflow.org/),
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.1