README.md

In taylorokonek/stbench: Fully Bayesian Benchmarking for spatio-temporal models

stbench

Functions to fit various spatio-temporal GLMMs in Template Model Builder (TMB), and produce fully Bayesian benchmarked estimates. Additional functionality includes various benchmarking approaches applied to posterior samples.

Overview

stbench is an open-source R package for producing fully Bayesian benchmarked estimates for hierarchical models in TMB. The package currently supports spatial and spatio-temporal modeling of binomial count data of under-5 mortality (U5MR), spatial models for generic binomial count data, and spatial models for normally distributed outcomes.

Current model implementations:

• U5MR
• Single-survey
  • Space-only
  • Binomial benched/unbenched
  • Time-only
  • Binomial benched/unbenched
  • Space-time
  • Binomial benched/unbenched
  • BetaBinomial benched/unbenched

• Multi-survey

  • Space-time
  • Binomial benched/unbenched

• Generic binary outcomes

• Single survey

  • Space-only
  • Binomial benched/unbenched

• Normally distributed outcomes

• Single survey
  • Space-only
  • benched/unbenched

Details can be found in the functions fit_u5mr, fit_binary, and fit_normal, respectively.

Current functions available that apply benchmarking methods to samples from a distribution include:

• benchmark_sampler: Fully Bayesian benchmarking via a rejection sampler, described in Okonek and Wakefield, 2022

• benchmark_bayesest: Constrained Bayes estimate approach to benchmarking, described in Datta et al., 2011

• benchmark_mh: Fully Bayesian benchmarking via a Metropolis-Hastings algorithm.

Installation

The current development version can be installed using devtools::install_github():

devtools::install_github(repo="taylorokonek/stbench")

taylorokonek/stbench documentation built on Jan. 7, 2025, 11:13 p.m.