magp: Mapping-Based Additive Gaussian Process Models

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>.

Version:	0.8.0
Imports:	Rcpp, nloptr, stats
LinkingTo:	Rcpp
Suggests:	testthat (≥ 3.0.0)
Published:	2026-09-03
DOI:	10.32614/CRAN.package.magp (may not be active yet)
Author:	Tony Wang [aut, cre, cph], Qian Xiao [aut, cph], Yaping Wang [cph], Abhyuday Mandal [cph], Xinwei Deng [cph]
Maintainer:	Tony Wang <wangtony883 at gmail.com>
License:	MIT + file LICENSE
NeedsCompilation:	yes
Materials:	README, NEWS
CRAN checks:	magp results

Documentation:

Reference manual:

magp.html , magp.pdf

Downloads:

Package source:	magp_0.8.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): magp_0.8.0.tgz, r-oldrel (arm64): magp_0.8.0.tgz, r-release (x86_64): magp_0.8.0.tgz, r-oldrel (x86_64): magp_0.8.0.tgz

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