The Bayesian modelling of relative sea-level data using a comprehensive approach that incorporates various statistical models within a unifying framework. Details regarding each statistical models; linear regression (Ashe et al 2019) <doi:10.1016/j.quascirev.2018.10.032>, change point models (Cahill et al 2015) <doi:10.1088/1748-9326/10/8/084002>, integrated Gaussian process models (Cahill et al 2015) <doi:10.1214/15-AOAS824>, temporal splines (Upton et al 2023) <doi:10.48550/arXiv.2301.09556>, spatio-temporal splines (Upton et al 2023) <doi:10.48550/arXiv.2301.09556> and generalised additive models (Upton et al 2023) <doi:10.48550/arXiv.2301.09556>. This package facilitates data loading, model fitting and result summarisation. Notably, it accommodates the inherent measurement errors found in relative sea-level data across multiple dimensions, allowing for their inclusion in the statistical models.
Version: 0.1.1 Depends: R (≥ 2.10) Imports: data.table, dplyr, fastDummies, fields, geosphere, ggplot2, magrittr, ncdf4, plyr, posterior, purrr, R2jags, stringr, tidybayes, tidyr Suggests: knitr, rmarkdown, testthat, vdiffr Published: 2023-06-15 DOI: 10.32614/CRAN.package.reslr Author: Maeve Upton [cph, aut, cre], Andrew Parnell [aut], Niamh Cahill [aut] Maintainer: Maeve Upton <uptonmaeve010 at gmail.com> BugReports: https://github.com/maeveupton/reslr/issues License: MIT + file LICENSE URL: https://github.com/maeveupton/reslr, https://maeveupton.github.io/reslr/ NeedsCompilation: no Language: en-US Materials: NEWS CRAN checks: reslr results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=reslr to link to this page.
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