Modelling Relative Sea Level Data
Overview
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 2025) doi:10.32614/RJ-2024-018, spatio-temporal splines (Upton et al 2025) doi:10.32614/RJ-2024-018 and generalised additive models (Upton et al 2025) doi:10.1093/jrsssc/qlae044. 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.
Install
Health
- OK13 OK2026-09-12
- ERROR12 OK, 1 ERROR2026-09-11
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-07-30
Show 9 earlier changesHide 9 earlier changes
- ERROR12 OK, 1 ERROR2026-07-29
- OK13 OK2026-06-25
- ERROR12 OK, 1 ERROR2026-06-24
- OK13 OK2026-06-08
- ERROR12 OK, 1 ERROR2026-06-07
- OK9 OK2026-05-27
- NOTE11 OK, 2 NOTE2026-05-21
- ERROR9 OK, 1 NOTE, 3 ERROR2026-05-11
- NOTE12 OK, 2 NOTE2026-03-10
Documentation
- Help pages
- 10 (1 internal)
- Examples that run
- 13%1 run in a plain check · 7 only when donttest examples are enabled (R CMD check --as-cran)
- Exported functions with examples
- 75%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Repository
Stars over time
Forks over time
Releases over time
Issues over time
9 development-tooling and community-health practices detected across 6 families in the upstream repository
Checks run against github.com/maeveupton/reslr on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
GitHub Pages site last deployed 2026-05-26 · The repository has a site deploy workflow
Show all practicesHide 2 practices
Dependencies
Installing reslr adds 56 packages (31 with compiled code, 54 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- unarchivedReturned to CRAN2026-05-26
- 0.2.0Latest
- archivedRemoved from CRAN2026-05-25issues were not corrected in time Also policy violation on Internet access
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.1.12023-06-15
- RR 4.3.0 released · 2023-04-21
Package metadata
- First published
- 2023-06-15
- Total releases
- 2 / 3 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 9.7 KB / 1 file
- Download size
- 2.9 MB
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("reslr")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.