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

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

Title: Distributed Gaussian process calculations
Authors@R: 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")))
Description: 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
Author: Christopher Paciorek [aut, cre], Benjamin Lipshitz [aut],
Prabhat [ctb], Cari Kaufman [ctb], Tina Zhuo [ctb], Rollin

Authors@R: 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")))
Description: 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
Author: Christopher Paciorek [aut, cre],