{ "_id": "6a1ec058b25058d4daae1dd3", "Package": "deepspat", "Type": "Package", "Title": "Deep Compositional Spatial Models", "Date": "2025-11-20", "Version": "0.3.1", "Authors@R": "c(person(given = \"Andrew\", family = \"Zammit-Mangion\", role = \"aut\"), \nperson(given = \"Quan\", family = \"Vu\", role = c(\"aut\", \"cre\"),\nemail = \"quanvustats@gmail.com\"),\nperson(given = \"Xuanjie\", family = \"Shao\", role = \"aut\")\n)", "Maintainer": "Quan Vu ", "Description": "Deep compositional spatial models are standard spatial\ncovariance models coupled with an injective warping function of\nthe spatial domain. The warping function is constructed through\na composition of multiple elemental injective functions in a\ndeep-learning framework. The package implements two cases for\nthe univariate setting; first, when these warping functions are\nknown up to some weights that need to be estimated, and,\nsecond, when the weights in each layer are random. In the\nmultivariate setting only the former case is available.\nEstimation and inference is done using `tensorflow`, which\nmakes use of graphics processing units. For more details see\nZammit-Mangion et al. (2022)\n, Vu et al. (2022)\n, Vu et al. (2023)\n, and Shao et al. 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