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The rust package implements the multivariate generalized ratio-of-uniforms method of simulating random variates from a d-dimensional continuous distribution. The user specifies (the log of) a positive target function f that is proportional to the density function of the distribution.
We use the function ru to simulate a sample of size 1000 from a two-dimensional standard normal distribution with strong positive correlation between the components. Of course, this particular example is purely illustrative: there are better ways to simulate from a multivariate normal distribution.
rho <- 0.9
covmat <- matrix(c(1, rho, rho, 1), 2, 2)
log_dmvnorm <- function(x, mean = rep(0, d), sigma = diag(d)) {
x <- matrix(x, ncol = length(x))
d <- ncol(x)
- 0.5 * (x - mean) %*% solve(sigma) %*% t(x - mean)
}
x <- ru(logf = log_dmvnorm, sigma = covmat, d = 2, n = 1000, init = c(0, 0))From version 1.2.0 onwards the faster function ru_rcpp can be used. See the vignette “Rusting Faster: Simulation using Rcpp” for details.
# Create an external pointer to a C++ function to evaluate the log-density.
ptr_bvn <- create_xptr("logdnorm2")
# Pass the external pointer to `ru_rcpp`.
x <- ru_rcpp(logf = ptr_bvn, rho = rho, d = 2, n = 1000, init = c(0, 0))To get the current released version from CRAN:
See vignette("rust-a-vignette", package = "rust") for an overview of the package, vignette("rust-b-when-to-use-vignette", package = "rust") for guidance on when rust can be used and vignette("rust-c-using-rcpp-vignette", package = "rust") for information on how to take advantage of the Rcpp package.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.