{"id":23509376,"url":"https://github.com/paulnorthrop/threshr","last_synced_at":"2025-09-02T00:38:44.324Z","repository":{"id":56936055,"uuid":"99112563","full_name":"paulnorthrop/threshr","owner":"paulnorthrop","description":"Threshold Selection and Uncertainty for Extreme Value Analysis","archived":false,"fork":false,"pushed_at":"2025-02-05T15:03:57.000Z","size":7489,"stargazers_count":7,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-06-28T02:48:58.196Z","etag":null,"topics":["extreme-value-statistics","extremes","generalized","inference","pareto","plot","prediction","threshold","threshold-selection","uncertainty"],"latest_commit_sha":null,"homepage":"https://paulnorthrop.github.io/threshr/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/paulnorthrop.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2017-08-02T12:11:01.000Z","updated_at":"2025-05-11T15:33:15.000Z","dependencies_parsed_at":"2023-01-22T04:50:02.007Z","dependency_job_id":"88e9dbca-0a8f-49d1-96d7-e2f41c7c201c","html_url":"https://github.com/paulnorthrop/threshr","commit_stats":{"total_commits":421,"total_committers":1,"mean_commits":421.0,"dds":0.0,"last_synced_commit":"5199755d0181178f7a8267197ac58229f0155849"},"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"purl":"pkg:github/paulnorthrop/threshr","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paulnorthrop%2Fthreshr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paulnorthrop%2Fthreshr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paulnorthrop%2Fthreshr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paulnorthrop%2Fthreshr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/paulnorthrop","download_url":"https://codeload.github.com/paulnorthrop/threshr/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/paulnorthrop%2Fthreshr/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":273141401,"owners_count":25052802,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-01T02:00:09.058Z","response_time":120,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["extreme-value-statistics","extremes","generalized","inference","pareto","plot","prediction","threshold","threshold-selection","uncertainty"],"created_at":"2024-12-25T11:43:47.156Z","updated_at":"2025-09-02T00:38:44.306Z","avatar_url":"https://github.com/paulnorthrop.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"README-\"\n)\n```\n\n# threshr\n\n[![AppVeyor Build Status](https://ci.appveyor.com/api/projects/status/github/paulnorthrop/threshr?branch=master\u0026svg=true)](https://ci.appveyor.com/project/paulnorthrop/threshr)\n[![R-CMD-check](https://github.com/paulnorthrop/threshr/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/paulnorthrop/threshr/actions/workflows/R-CMD-check.yaml)\n[![Coverage Status](https://codecov.io/github/paulnorthrop/threshr/coverage.svg?branch=master)](https://app.codecov.io/github/paulnorthrop/threshr?branch=master)\n[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/threshr)](https://cran.r-project.org/package=threshr)\n[![Downloads (monthly)](https://cranlogs.r-pkg.org/badges/threshr?color=brightgreen)](https://cran.r-project.org/package=threshr)\n[![Downloads (total)](https://cranlogs.r-pkg.org/badges/grand-total/threshr?color=brightgreen)](https://cran.r-project.org/package=threshr)\n\n## Threshold Selection and Uncertainty for Extreme Value Analysis\n\n### What does threshr do?\n\nThe `threshr` package deals primarily with the selection of thresholds for use in extreme value models. It also performs predictive inferences about future extreme values. These inferences can either be based on a single threshold or on a weighted average of inferences from multiple thresholds.  The weighting reflects an estimated measure of the predictive performance of the threshold and can incorporate prior probabilities supplied by a user.  At the moment only the simplest case, where the data can be treated as independent identically distributed observations, is considered, as described in [Northrop et al. (2017)](https://doi.org/10.1111/rssc.12159).  Future releases will tackle more general situations.  \n\n### A simple example\n\nThe main function in the threshr package is `ithresh`.  It uses Bayesian leave-one-out cross-validation to compare the extreme value predictive ability resulting from the use of each of a user-supplied set of thresholds.  The following code produces a threshold diagnostic plot using a dataset `gom` containing 315 storm peak significant waveheights.  We set a vector `u_vec` of thresholds; call `ithresh`, supplying the data and thresholds; and use then plot the results. In this minimal example (`ithresh` has further arguments) thresholds are judged in terms of the quality of prediction of whether the validation observation lies above the highest threshold in `u_vec` and, if it does, how much it exceeds this highest threshold.\n\n```{r, eval = FALSE}\nlibrary(threshr)\nu_vec_gom \u003c- quantile(gom, probs = seq(0, 0.9, by = 0.05))\ngom_cv \u003c- ithresh(data = gom, u_vec = u_vec_gom)\nplot(gom_cv)\n```\n\n### Installation\n\nTo get the current released version from CRAN:\n\n```{r installation, eval = FALSE}\ninstall.packages(\"threshr\")\n```\n\n### Vignette\n\nSee `vignette(\"threshr-vignette\", package = \"threshr\")` for an overview of the package.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaulnorthrop%2Fthreshr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpaulnorthrop%2Fthreshr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaulnorthrop%2Fthreshr/lists"}