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  "Description": "Provides tools for conditional and spatially dependent\ndensity estimation using Spatial Logistic Gaussian Processes\n(SLGPs). The approach represents probability densities through\nfinite-rank Gaussian process priors transformed via a spatial\nlogistic density transformation, enabling flexible\nnon-parametric modeling of heterogeneous data. Functionality\nincludes density prediction, quantile and moment estimation,\nsampling methods, and preprocessing routines for basis\nfunctions. Applications arise in spatial statistics, machine\nlearning, and uncertainty quantification. The methodology\nbuilds on the framework of Leonard (1978)\n<doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988)\n<doi:10.1080/01621459.1988.10478625>, Tokdar (2007)\n<doi:10.1198/106186007X210206>, Tokdar (2010)\n<doi:10.1214/10-BA605>, and is further aligned with recent\ndevelopments in Bayesian non-parametric modelling: see Gautier\n(2023) <https://boristheses.unibe.ch/4377/>, and Gautier (2025)\n<doi:10.48550/arXiv.2110.02876>).",
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