Package: reslr 0.2.0

reslr: Modelling Relative Sea Level Data

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.

Authors:Maeve Upton [cph, aut, cre], Andrew Parnell [aut], Niamh Cahill [aut]

reslr_0.2.0.tar.gz
reslr_0.2.0.zip(r-4.7-any)reslr_0.2.0.zip(r-4.6-any)reslr_0.2.0.zip(r-4.5-any)
reslr_0.2.0.tgz(r-4.6-any)reslr_0.2.0.tgz(r-4.5-any)
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reslr_0.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
reslr/json (API)

# Install 'reslr' in R:
install.packages('reslr', repos = c('https://maeveupton.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/maeveupton/reslr/issues

Pkgdown/docs site:https://maeveupton.github.io

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

jagscpp

5.66 score 4 stars 38 scripts 409 downloads 4 exports 58 dependencies

Last updated from:9e22beb382. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK265
source / vignettesOK328
linux-releaseOK189
macos-releaseOK236
macos-oldrelOK277
windows-develOK277
windows-releaseOK276
windows-oldrelOK272
wasm-releaseOK106

Exports:%>%cross_val_checkreslr_loadreslr_mcmc

Dependencies:abindarrayhelpersbackportsbootcheckmateclicodacpp11data.tabledistributionaldotCall64dplyrfarverfastDummiesfieldsgenericsgeosphereggdistggplot2gluegtableisobandlabelinglatticelifecyclemagrittrmapsmatrixStatsncdf4numDerivpillarpkgconfigplyrposteriorpurrrquadprogR2jagsR2WinBUGSR6RColorBrewerRcpprjagsrlangS7scalesspamstringistringrsvUnittensorAtibbletidybayestidyrtidyselectutf8vctrsviridisLitewithr

reslr: quick start guide
Step 1: install reslr | Step 2: load in the data into reslr | Step 3: plot the data | Step 4: Run your statistical model and check convergence | Step 5: Plot the results
Rendered fromquick_start.Rmdusingknitr::rmarkdown

Last update: 2026-05-14
Started: 2023-05-11

reslr: Statistical Models for examining Relative Sea Level Change in R
Introduction | Installation of the reslr package | Considerations before running reslr | Installating JAGS software | Working with scripts | Inputting User's data | Tide Gauge Data | Glacial Isostatic Adjustment (GIA) | Example Data Set | How to run reslr | Errors-in-Variables Simple Linear Regression ("eiv_slr_t") | Errors-in-Variable Change Point Model ("eiv_cp_t") | Errors-in-Variable Integrated Gaussian Process Model ("eiv_igp_t") | Noisy input spline in time ("ni_spline_t") | Noisy input spline in space time ("ni_spline_st") | Noisy Input Generalised Additive Model for decomposition of response signal ("ni_gam_decomp") | Appendix - suggested reading
Rendered fromreslr.Rmdusingknitr::rmarkdown

Last update: 2026-05-14
Started: 2023-03-13

reslr: advanced
Introduction | Errors in Variables Integrated Gaussian Process with detrended data | Using a different input age unit | Including Tide Gauge data | Plotting techniques | Accessing the posterior samples | Smoothing settings for splines | Cross Validation tests for Spline and NI-GAM
Rendered fromadvanced_reslr.Rmdusingknitr::rmarkdown

Last update: 2024-02-12
Started: 2023-05-11