meshed: Bayesian Regression with Meshed Gaussian Processes

Fits Bayesian spatial or spatiotemporal multivariate regression models based on latent Meshed Gaussian Processes (MGP) as described in Peruzzi, Banerjee, Finley (2020) <doi:10.1080/01621459.2020.1833889> and Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Version: 0.1.4
Imports: Rcpp (≥ 1.0.5), stats, dplyr, glue, rlang, magrittr
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggplot2, abind, rmarkdown, knitr, tidyr
Published: 2021-10-06
Author: Michele Peruzzi
Maintainer: Michele Peruzzi <michele.peruzzi at duke.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: meshed results

Downloads:

Reference manual: meshed.pdf
Vignettes: MGPs for multivariate data at irregularly spaced locations
MGPs for univariate spatial gridded data
MGPs for univariate data at irregularly spaced locations
MGPs for univariate spatial non-Gaussian data
Package source: meshed_0.1.4.tar.gz
Windows binaries: r-devel: meshed_0.1.4.zip, r-devel-UCRT: meshed_0.1.4.zip, r-release: meshed_0.1.4.zip, r-oldrel: meshed_0.1.2.zip
macOS binaries: r-release (arm64): meshed_0.1.2.tgz, r-release (x86_64): meshed_0.1.4.tgz, r-oldrel: meshed_0.1.4.tgz
Old sources: meshed archive

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