Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) "Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator" tools:::Rd_expr_doi("10.48550/arxiv.2311.09446"). These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the "ht" function (for hypothesis testing) and the "ci" function (for constructing a confidence interval for one-dimensional parameters).
Maintainer: Joonha Park j.park@ku.edu (ORCID)
Park, J. (2025) Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator tools:::Rd_expr_doi("10.48550/arxiv.2311.09446")