{ "_id": "6a1556abacfb0bcc41d5e16f", "Package": "BKTR", "Version": "0.2.0.9000", "Title": "Bayesian Kernelized Tensor Regression", "Authors@R": "c(\nperson(\"Julien\", \"Lanthier\", email = \"julien.lanthier@hec.ca\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0009-0008-8728-4996\")),\nperson(\"Mengying\", \"Lei\", role = \"aut\", comment = c(ORCID = \"0000-0001-7343-3323\")),\nperson(\"Aurélie\", \"Labbe\", role = \"aut\", comment = c(ORCID = \"0000-0002-4207-8143\")),\nperson(\"Lijun\", \"Sun\", role = \"aut\", comment = c(ORCID = \"0000-0001-9488-0712\"))\n)", "Description": "Facilitates scalable spatiotemporally varying coefficient\nmodelling with Bayesian kernelized tensor regression. The\nimportant features of this package are: (a) Enabling local\ntemporal and spatial modeling of the relationship between the\nresponse variable and covariates. (b) Implementing the model\ndescribed by Lei et al. (2023) .\n(c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm\nto sample from the posterior distribution of the model\nparameters. (d) Employing a tensor decomposition to reduce the\nnumber of estimated parameters. 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