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Here is an R function that produces a Metropolis-Hastings sample for the univariate log-target f when the later is defined outside as another function. And when using a Gaussian random walk with scale one as proposal. (Inspired from a X validated question.)

m<-function(T,y=rnorm(1))ifelse(rep(T>1,T),
c(y*{f({z<-m(T-1)}[1])-f(y+z[1])<rexp(1)}+z[1],z),y)


The function is definitely not optimal, crashes for values of T larger than 580 (unless one modifies the stack size), and operates the most basic version of a Metropolis-Hastings algorithm. But as a codegolf challenge (on a late plane ride), this was a fun exercise.