The commonsMath package provides access to the Apache Commons Mathematics Library. It can can accessed via the:
We demonstrate below how to use it in:
library("rscala")
s <- scala("commonsMath")
rng1 <- s$.new_org.apache.commons.math3.random.RandomDataGenerator()
rng1$reSeed(7342L)
rng1$nextGaussian(0,1)
## [1] -1.167763
library("rJava")
.jinit(Sys.glob(file.path(system.file(package="commonsMath"), "java", "*.jar")))
rng2 <- .jnew("org.apache.commons.math3.random.RandomDataGenerator")
rng2$reSeed(.jlong(7342L))
rng2$nextGaussian(0,1)
## [1] -1.167763
The DESCRIPTION
should have Imports: rscala, commonsMath
.
The NAMESPACE
should have import(rscala)
.
Define an .onLoad
function like the following:
.onLoad <- function(libname, pkgname) {
s <- scala("commonsMath")
assign("s", s, envir = parent.env(environment()))
}
Package functions can then assess classes and methods from the commonsMath package, e.g.:
rstdnorm <- function() {
rng1 <- s$.new_org.apache.commons.math3.random.RandomDataGenerator()
rng1$nextGaussian(0.0,1.0)
}
The DESCRIPTION
should have Imports: rJava, commonsMath
.
The NAMESPACE
should first have import(rscala)
and then have import(commonsMath)
.
Define an .onLoad
function like the following:
.onLoad <- function(libname, pkgname) {
.jpackage(pkgname, lib.loc=libname)
}
Package functions can then assess classes and methods from the commonsMath package, e.g.:
rstdnorm <- function() {
rng2 <- .jnew("org.apache.commons.math3.random.RandomDataGenerator")
rng2$nextGaussian(0,1)
}