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  "Title": "Log-Gaussian Cox Process Models with Approximations",
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  "Description": "Supports modelling case data to facilitate. The package\nprovides automated computational grid generation over an area\nof interest with methods to map covariates between geographies,\nmodel fitting including spatially aggregated case counts, and\npredictions and visualisation. Monte Carlo maximum likelihood\nis the main fitting method with a low-rank approximation for\nGaussian processes described by Solin and Särkkä (2020)\n<doi:10.1007/s11222-019-09886-w> and a stochastic partial\ndifferential equation approximation. Bayesian methods are also\nprovided for some methods. Log-Gaussian Cox Processes are\ndescribed by Diggle et al. (2013) <doi:10.1214/13-STS441>.",
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