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$nextGaussian(0,1)library("rJava")
.jinit(Sys.glob(file.path(system.file(package="commonsMath"), "java", "*.jar")))
rng2 <- .jnew("org.apache.commons.math3.random.RandomDataGenerator")
rng2$reSeed(.jlong(7342L))
rng2$nextGaussian(0,1)The DESCRIPTION should have Imports: rscala, commonsMath.
The NAMESPACE should have import(rscala).
Define an .onLoad function like the following:
.onLoad <- function(libname, pkgname) {
s <- scala("commonsMath")
assign("s", s, envir = parent.env(environment()))
}Package functions can then assess classes and methods from the commonsMath package, e.g.:
rstdnorm <- function() {
rng1 <- s$.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)
}
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