Package: BayesNSGP 0.1.2

BayesNSGP: Bayesian Analysis of Non-Stationary Gaussian Process Models

Enables off-the-shelf functionality for fully Bayesian, nonstationary Gaussian process modeling. The approach to nonstationary modeling involves a closed-form, convolution-based covariance function with spatially-varying parameters; these parameter processes can be specified either deterministically (using covariates or basis functions) or stochastically (using approximate Gaussian processes). Stationary Gaussian processes are a special case of our methodology, and we furthermore implement approximate Gaussian process inference to account for very large spatial data sets (Finley, et al (2017) <arxiv:1702.00434v2>). Bayesian inference is carried out using Markov chain Monte Carlo methods via the 'nimble' package, and posterior prediction for the Gaussian process at unobserved locations is provided as a post-processing step.

Authors:Daniel Turek, Mark Risser

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29 exports 1 stars 0.09 score 48 dependencies 5 scripts 259 downloads

Last updated 3 years agofrom:2fe1901919. Checks:OK: 7. Indexed: yes.

Exports:calcQFcalculateAD_nscalculateU_nsconditionLatentObsdetermineNeighborsdmnorm_nngpdmnorm_sgvinverseEigenmatern_corrnimble_sparse_cholnimble_sparse_crossprodnimble_sparse_solvenimble_sparse_tcrossprodnsCorrnsCrosscorrnsCrossdistnsCrossdist3dnsDistnsDist3dnsgpModelnsgpPredictorderCoordinatesMMDR_sparse_cholR_sparse_crossprodR_sparse_solveR_sparse_tcrossprodrmnorm_nngprmnorm_sgvsgvSetup

Dependencies:clicodacolorspacecpp11DBIdplyrfansifarverFNNgenericsggplot2gluegtableigraphisobandlabelinglatticelifecyclelpSolvemagrittrMASSMatrixmgcvminqamitoolsmunsellnimblenlmenumDerivpillarpkgconfigpracmaproxyR6RColorBrewerRcppRcppArmadillorlangscalesStatMatchsurveysurvivaltibbletidyselectutf8vctrsviridisLitewithr