Implements several spatial and spatio-temporal scalable disease mapping models for high-dimensional count data using the INLA technique for approximate Bayesian inference in latent Gaussian models (Orozco-Acosta et al., 2021 <doi:10.1016/j.spasta.2021.100496>; Orozco-Acosta et al., 2022 <arXiv:2201.08323> and Vicente et al., 2022 <arXiv:2210.14849>). The creation and develpment of this package has been supported by Project MTM2017-82553-R (AEI/FEDER, UE) and Project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001).
crayon, fastDummies, future, future.apply, MASS, Matrix,
methods, parallel, RColorBrewer, Rdpack, sf, spatialreg, spdep,
stats, utils, rlist

Fast Linear Models for Objects from the 'bigmemory' Package
A reimplementation of the fastLm() functionality of 'RcppEigen' for
big.matrix objects for fast out-of-memory linear model fitting.

c(
person("Jared", "Huling", , "jaredhuling@gmail.com", c("aut", "cre")),
person("Douglas", "Bates", , , c("ctb")),
person("Dirk", "Eddelbuettel", , , c("ctb")),
person("Romain", "Francois", , , c("ctb")),
person("Yixuan", "Qiu", , , c("ctb"))
)
Jared Huling [aut, cre],
Douglas Bates [ctb],
Dirk Eddelbuettel [ctb],
Romain Francois [ctb],
Yixuan Qiu [ctb]

c(person("Christopher", "Paciorek", email =
"paciorek@stat.berkeley.edu", role = c("aut", "cre")),
person("Benjamin", "Lipshitz", role = c("aut")), person(family
= "Prabhat", role = c("ctb")), person("Cari", "Kaufman", role =
c("ctb")), person("Tina", "Zhuo", role = c("ctb")),
person("Rollin", "Thomas", role = c("ctb")))
bigGP distributes Gaussian process calculations across
nodes in a distributed memory setting, using Rmpi. The class
provides high-level methods for maximum likelihood with normal
data, prediction, calculation of uncertainty (i.e., posterior
covariance calculations), and simulation of realizations. In
addition, bigGP provides an API for basic matrix calculations
with distributed covariance matrices, including Cholesky
decomposition, back/forwardsolve, crossproduct, and matrix
multiplication.
'auxil.R' 'bigGP.R' 'collectDistribute.R'
'distributedComputation.R' 'krigeProblem.R'
Christopher Paciorek [aut, cre], Benjamin Lipshitz [aut],
Prabhat [ctb], Cari Kaufman [ctb], Tina Zhuo [ctb], Rollin
Thomas [ctb]