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ARCokrig  

Autoregressive Cokriging Models for Multifidelity Codes
View on CRAN: Click here


Download and install ARCokrig package within the R console
Install from CRAN:
install.packages("ARCokrig")

Install from Github:
library("remotes")
install_github("cran/ARCokrig")

Install by package version:
library("remotes")
install_version("ARCokrig", "0.1.2")



Attach the package and use:
library("ARCokrig")
Maintained by
Pulong Ma
[Scholar Profile | Author Map]
First Published: 2020-07-08
Latest Update: 2021-12-02
Description:
For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) . The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) .
How to cite:
Pulong Ma (2020). ARCokrig: Autoregressive Cokriging Models for Multifidelity Codes. R package version 0.1.2, https://cran.r-project.org/web/packages/ARCokrig. Accessed 09 May. 2025.
Previous versions and publish date:
0.1.1 (2020-07-08 10:30)
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Complete documentation for ARCokrig
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