Stilt: Easy emulation of time series AR(1) computer model output in multidimensional parameter space

Roman Olson, Kelsey L. Ruckert, Won Chang, Klaus Keller, Murali Haran, Soon Il An

Research output: Contribution to journalArticle


Statistically approximating or "emulating" time series model output in parameter space is a common problem in climate science and other fields. There are many packages for spatio-temporal modeling. However, they often lack focus on time series, and exhibit statistical complexity. Here, we present the R package stilt designed for simplified AR(1) time series Gaussian process emulation, and provide examples relevant to climate modelling. Notably absent is Markov chain Monte Carlo estimation - a challenging concept to many scientists. We keep the number of user choices to a minimum. Hence, the package can be useful pedagogically, while still applicable to real life emulation problems. We provide functions for emulator cross-validation, empirical coverage, prediction, as well as response surface plotting. While the examples focus on climate model emulation, the emulator is general and can be also used for kriging spatio-temporal data.

Original languageEnglish
Pages (from-to)209-225
Number of pages17
JournalR Journal
Issue number2
Publication statusPublished - 2019 Jan 1


All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

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