The R Journal: article published in 2018, volume 10:2

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 and Soon-Il An , The R Journal (2018) 10:2, pages 209-225.

Abstract 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.

Received: 2017-11-16; online 2018-12-07, supplementary material, (1.4 KiB)
CRAN packages: gstat, mlegp, spBayes, ramps, spTimer, RandomFields, stilt, fields, maps, spam, dotCall64
CRAN Task Views implied by cited CRAN packages: Spatial, SpatioTemporal, Bayesian, Multivariate, TimeSeries

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2018-049,
  author = {Roman Olson and Kelsey L. Ruckert and Won Chang and Klaus
          Keller and Murali Haran and Soon-Il An},
  title = {{Stilt: Easy Emulation of Time Series AR(1) Computer Model
          Output in Multidimensional Parameter Space}},
  year = {2018},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2018-049},
  url = {https://doi.org/10.32614/RJ-2018-049},
  pages = {209--225},
  volume = {10},
  number = {2}
}