cdriveraus/stanoptimis: Optimization and importance sampling for Stan models.

A small package containing a 3 step routine for fitting Stan models, and limited diagnostics. For complex models with low to moderate numbers of parameters, where the maximum a-posteriori estimate provides a useful starting point, this approach 'may' allow for faster sampling from the posterior. The optional step 1 uses differential evolution, linking to the DEoptim package. Step 2 then uses a BFGS optimizer from the mize package. Step 3 computes a Hessian, or approximation of the Hessian, and uses this either for: a) directly sampling from, for fast but inaccurate representation of the posterior. b) as the basis for an initial target distribution for an adaptive importance sampling procedure.

Getting started

Package details

AuthorCharles Driver
MaintainerCharles Driver <>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
cdriveraus/stanoptimis documentation built on July 26, 2019, 3:18 p.m.