Package: ErrorTracer 1.1.0

ErrorTracer: Bayesian Error Propagation and Forecast Uncertainty Decomposition

Provides a full pipeline from regularized or standard regression models (elastic net, linear models, generalized linear models, random forests) to informed Bayesian priors, structured forecast uncertainty decomposition (parameter / environmental / residual, plus a temporal component when the model carries an autocorrelation term), and forecast shelf life analysis (the quantification of when a forecast becomes uninformative). Designed for ecological and genomic forecasting with climate or environmental covariates. Methods build on Bürkner (2017) <doi:10.18637/jss.v080.i01> for Bayesian regression via 'Stan', Friedman, Hastie, and Tibshirani (2010) <doi:10.18637/jss.v033.i01> for elastic net regularization, Wright and Ziegler (2017) <doi:10.18637/jss.v077.i01> for random forests, and Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4> for leave-one-out cross-validation.

Authors:Luis Javier Madrigal-Roca [aut, cre], John Kelly [aut]

ErrorTracer_1.1.0.tar.gz
ErrorTracer_1.1.0.zip(r-4.7)ErrorTracer_1.1.0.zip(r-4.6)ErrorTracer_1.1.0.zip(r-4.5)
ErrorTracer_1.1.0.tgz(r-4.6-any)ErrorTracer_1.1.0.tgz(r-4.5-any)
ErrorTracer_1.1.0.tar.gz(r-4.7-any)ErrorTracer_1.1.0.tar.gz(r-4.6-any)
ErrorTracer_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ErrorTracer/json (API)

# Install 'ErrorTracer' in R:
install.packages('ErrorTracer', repos = c('https://luismadrigal98.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/luismadrigal98/errortracer/issues

On CRAN:

Conda:

5.00 score 1 stars 4 scripts 173 downloads 18 exports 75 dependencies

Last updated from:c97a46348b. Checks:7 ERROR, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64ERROR186
source / vignettesOK230
linux-release-x86_64ERROR167
macos-release-arm64ERROR136
macos-oldrel-arm64ERROR145
windows-develERROR103
windows-releaseERROR113
windows-oldrelERROR119
wasm-releaseOK178

Exports:decompose_uncertaintyet_calibrateet_diagnoseet_fitet_plot_calibrationet_plot_coefficientset_plot_decompositionet_plot_forecastet_plot_prior_posterioret_plot_sensitivityet_plot_shelf_lifeet_predictet_sensitivity_profileet_themeextract_priorsshelf_lifestandardizeunstandardize

Dependencies:abindbackportsbayesplotBHbridgesamplingbrmsBrobdingnagcallrcheckmateclicodacodetoolscpp11descdigestdistributionaldplyrfarverfuturefuture.applygenericsggplot2ggridgesglobalsgluegridExtragtableinlineisobandlabelinglatticelifecyclelistenvloomagrittrMatrixmatrixStatsmgcvmvtnormnleqslvnlmenumDerivotelparallellypillarpkgbuildpkgconfigplyrposteriorprocessxpspurrrQuickJSRR6RColorBrewerRcppRcppEigenRcppParallelreshape2rlangrstanrstantoolsS7scalesStanHeadersstringistringrtensorAtibbletidyrtidyselectutf8vctrsviridisLitewithr

A one-predictor worked example
What this vignette is for | Setup: simulate one predictor, one response — over years | Stage 1: turn a quick frequentist fit into a prior | Stage 2: Bayesian refit with the informed prior | Stage 3: posterior prediction across the forecast horizon | Stage 4: decompose the variance over time | Stage 5: when does the forecast become uninformative? | The whole-pipeline figure | What this example does not show | Session info
Rendered fromworked_example.Rmdusingknitr::rmarkdown

Last update: 2026-05-23
Started: 2026-05-23

Getting Started with ErrorTracer
Introduction | The simulated dataset | Setup | Exploring the dataset | Visualise the training data | Single-cluster workflow: Cluster A | Step 1 — Elastic net for prior extraction | Step 2 — Extract informed priors | Step 3 — Fit the Bayesian model | Step 4 — Diagnose convergence and model fit | Step 5 — Predict with environmental noise propagation | Step 6 — Uncertainty decomposition | Step 7 — Forecast shelf life | Step 8 — Calibration assessment | Visualisations | Forecast fan chart | Prior versus posterior | Forest plot: Bayesian vs. elastic net | Parameter recovery validation | Multi-cluster grouped workflow | Fitting | Diagnostics | Prediction and decomposition | Shelf life comparison | Calibration and forecast plots | Long-horizon forecasting with GCM projections | Using lm instead of glmnet | Standardising and back-transforming | Summary | Session information
Rendered fromintroduction.Rmdusingknitr::rmarkdown

Last update: 2026-05-16
Started: 2026-04-13