Authors@R: person("Jeroen", "Ooms", role = c("aut", "cre"), email = "jeroen@berkeley.edu",
package can now be compiled either with V8 version 6 or 7 (LTS) from
nodejs or with the legacy 3.14/3.15 branch of V8.
URL: https://github.com/jeroen/v8 (devel) https://v8.dev (upstream)

Title: Embedded JavaScript and WebAssembly Engine for R
Description: An R interface to V8: Google's open source JavaScript and WebAssembly
engine. This package can be compiled either with V8 version 6 and up, a NodeJS
shared library, or the legacy 3.14/3.15 branch of V8.
SystemRequirements: V8 engine version 6+ is needed for modern JS and
WASM support. On Debian / Ubuntu install either libv8-dev or
libnode-dev, on Fedora use v8-devel. The readme has
instructions for installing backports on Ubuntu Xenial and
Bionic. It is still possible to build this package against the
legacy libv8 version 3.14 branch, but these engines only

SystemRequirements: V8 engine version 6+ is needed for ES6 and WASM
support. On Debian / Ubuntu install either libv8-dev or
libnode-dev, on Fedora use v8-devel. Alternatively, we provide
static libv8 binaries most platforms, see the README for

engine. This package can be compiled either with V8 version 6 and up or NodeJS
support. On Linux you can also build against libv8-dev (Debian)
or v8-devel (Fedora). We also provide static libv8 binaries for
most platforms, see the README for details.

support. On Linux you can build against libv8-dev (Debian) or
v8-devel (Fedora). We also provide static libv8 binaries for

person("Jeroen", "Ooms", role = c("aut", "cre"), email = "jeroen@berkeley.edu",
person("Jan Marvin", "Garbuszus", role = "ctb"))

Title: Estimation of bayesian additive structured (quantile) regression
Author: Elisabeth Waldmann <ewaldma@uni-goettingen.de>, with
contributions from Udo Schroeder <udo.schroeder@uni-oldenburg.de>.
Depends: R (>= 1.8.0), BayesX, splines, Runuran, formula.tools
Description: This package provides a fast way of estimating Bayesian geoadditive (quantile) regression, using the idea of variational approximations.