This package implements in R the affine-invariant sampling method of Goodman & Weare (2010). This is a way of producing Monte-Carlo samples from a target distribution, which can be used for statistical inference.
In R, run
install.packages("rgw"). Note that the version hosted on CRAN may lag behind this one (see VERSION.md).
- Clone this repository.
- In a terminal, navigate to the
R CMD install rgw. Alternatively, in an R session, run
Here's the simple example that appears in the documentation:
# In this example, we'll sample from a simple 2D Gaussian. # Define the log-posterior function lnP = function(x) sum( dnorm(x, c(0,1), c(pi, exp(0.5)), log=TRUE) ) # Initialize an ensemble of 100 walkers. We'll take 100 steps, saving the ensemble after each. nwalk = 100 post = array(NA, dim=c(2, nwalk, 101)) post[1,,1] = rnorm(nwalk, 0, 0.1) post[2,,1] = rnorm(nwalk, 1, 0.1) # Run post = GoodmanWeare.rem(post, lnP) # Plot the final ensemble plot(post[1,,101], post[2,,101]) # Look at the trace of each parameter for one of the walkers. plot(post[1,1,]) plot(post[2,1,]) # Go on to get confidence intervals, make niftier plots, etc.
Open an issue.