This package allows one to call from R the implementation of Masked Autoregressive Flows (MAFs) in the zuko python package (Rozet et al. 2023). It has been designed more specifically for use with the Infusion R package. Although the installation of the mafR package is trivial, you will need to install a Python environment with appropriate packages in order to be able to use mafR. See the installation instructions in the main documentation page for mafR.

Then, you should be able to call MAFs when using Infusion for simulation-based inference, as illustrated by the following toy example:

Preliminaries:

  library(Infusion)
  cat(cli::col_yellow("(!) This example uses cuda (!)"))
  config_mafR(torch_device="cuda")

Define function for sampling from 3-parameter gaussian mixture:

  myrnorm3 <- function(mu1,mu2,s2,sample.size=40L) {
    sam1 <- rnorm(n=sample.size,mean=mu1,sd=sqrt(s2))
    sam2 <- rnorm(n=sample.size,mean=mu2,sd=sqrt(s2))
    e_mu1 <- mean(sam1)
    e_mu2 <- mean(sam2)
    e_s2 <- mean(var(sam1),var(sam2))
    return(c(mean1=e_mu1,mean2=e_mu2,var=e_s2))
  } 

Simulate data, standing for the data to be analyzed:

  set.seed(123)
  Sobs <- myrnorm3(mu1=4,mu2=2,s2=1) 

Build initial reference table of simulated samples:

  parsp <- init_reftable(lower=c(mu1=2.8,mu2=1,s2=0.2), 
                         upper=c(mu1=5.2,mu2=3,s2=3))
  parsp <- cbind(parsp)
  simuls <- add_reftable(Simulate="myrnorm3", parsTable=parsp)

Perform initial inference of likelihood surface (note using="mafR"):

  densv <- infer_SLik_joint(simuls,stat.obs=Sobs, using="mafR")  

Carry on iterative workflow using inferred surface:

  slik_j <- MSL(densv, eval_RMSEs = FALSE) ## find the maximum of the log-likelihood surface
  slik_j <- refine(slik_j,maxit=5, update_projectors=TRUE) ## further steps as in a standard Infusion workflow