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:
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