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: 0.1.2
Imports: Rcpp (≥ 1.0.5), stats, dplyr, glue, rlang, magrittr
LinkingTo: Rcpp, RcppArmadillo
Suggests: ggplot2, abind, rmarkdown, knitr, tidyr
Published: 2021-06-22
Author: Michele Peruzzi
Maintainer: Michele Peruzzi <michele.peruzzi at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: meshed results


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_0.1.2.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): meshed_0.1.2.tgz, r-release (x86_64): meshed_0.1.2.tgz, r-oldrel: meshed_0.1.2.tgz
Old sources: meshed archive


Please use the canonical form to link to this page.