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>, Peruzzi, Banerjee, Dunson, and Finley (2021) <arXiv:2101.03579>, Peruzzi and Dunson (2022) <arXiv:2201.10080>. Funded by ERC grant 856506 and NIH grant R01ES028804.

Version:	0.2.1
Imports:	Rcpp (≥ 1.0.5), stats, dplyr, glue, rlang, magrittr
LinkingTo:	Rcpp, RcppArmadillo
Suggests:	ggplot2, abind, rmarkdown, knitr, tidyr
Published:	2022-01-28
Author:	Michele Peruzzi
Maintainer:	Michele Peruzzi <michele.peruzzi at duke.edu>
License:	GPL (≥ 3)
NeedsCompilation:	yes
Materials:	README NEWS
CRAN checks:	meshed results

Documentation:

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

Downloads:

Package source:	meshed_0.2.1.tar.gz
Windows binaries:	r-devel: meshed_0.2.1.zip, r-release: meshed_0.2.1.zip, r-oldrel: meshed_0.2.1.zip
macOS binaries:	r-release (arm64): meshed_0.2.1.tgz, r-oldrel (arm64): meshed_0.2.1.tgz, r-release (x86_64): meshed_0.2.1.tgz, r-oldrel (x86_64): meshed_0.2.1.tgz
Old sources:	meshed archive

Linking:

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