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When doing inference, the main computational\ndifficulty consists in evaluating the likelihood and in\nsampling from the full conditional of the spectral\ncoefficients, or equivalently, the latent space-time process.\nIn comparison to the traditional approach of using a\nspatio-temporal covariance function, the spectral SPDE approach\nis computationally advantageous. See Sigrist, Kuensch, and\nStahel (2015) for more information on\nthe methodology. This package aims at providing tools for two\ndifferent modeling approaches. First, the SPDE based\nspatio-temporal model can be used as a component in a\ncustomized hierarchical Bayesian model (HBM). The functions of\nthe package then provide parameterizations of the process part\nof the model as well as computationally efficient algorithms\nneeded for doing inference with the HBM. Alternatively, the\nadaptive MCMC algorithm implemented in the package can be used\nas an algorithm for doing inference without any additional\nmodeling. The MCMC algorithm supports data that follow a\nGaussian or a censored distribution with point mass at zero.\nCovariates can be included in the model through a regression\nterm.", "License": "GPL-2", "LazyData": "true", "Config/pak/sysreqs": "libfftw3-dev", "Repository": "https://fabsig.r-universe.dev", "Date/Publication": "2023-10-03 07:15:06 UTC", "RemoteUrl": "https://github.com/fabsig/spate", "RemoteRef": "HEAD", "RemoteSha": "d9a3aba3ee22cddefc4f728b4312d9a747ce079b", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-11 08:32:31 UTC", "User": "root" }, "MD5sum": "460dd0accb4323853a24bd5510c5f477", "_user": "fabsig", "_type": "src", "_file": "spate_1.7.5.tar.gz", "_fileid": "8c03e4621c92e6cb3769832d4b76c6bcb27d4e319b20c740fc2b24d29165a0be", "_filesize": 1508446, "_sha256": "8c03e4621c92e6cb3769832d4b76c6bcb27d4e319b20c740fc2b24d29165a0be", "_created": "2026-05-11T08:32:31.000Z", "_published": "2026-05-22T11:37:06.490Z", "_distro": "noble", "_jobs": [ { "job": 77371806916, 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