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magp  

Mapping-Based Additive Gaussian Process Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("magp", "0.12.0")



Attach the package and use:
library("magp")
Maintained by
Tony Wang
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-09-03
Latest Update: 2026-09-03
Description:
Fits mapping-based additive Gaussian process models for experiments in which each component has both a quantitative level and a position in an ordered sequence. Two model structures are available: a compact two-dimensional mapping and a full mapping with one fewer dimension than the number of components. Both models support parameter estimation, point prediction, and plug-in predictive uncertainty. Input checks validate the sequence data and apply consistent scaling to the quantitative inputs. Computationally intensive covariance and gradient calculations are implemented in C++ with 'Rcpp'. The model was introduced by Xiao et al. (2024) <doi:10.1080/01621459.2022.2123335>.
How to cite:
Tony Wang (2026). magp: Mapping-Based Additive Gaussian Process Models. R package version 0.12.0, https://cran.r-project.org/web/packages/magp. Accessed 04 Oct. 2026.
Previous versions and publish date:
(2026-09-24 18:10), 0.8.0 (2026-09-03 13:40)
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