{"id":17152985,"url":"https://github.com/geobosh/cvar","last_synced_at":"2025-04-13T12:43:33.062Z","repository":{"id":56936942,"uuid":"128628020","full_name":"GeoBosh/cvar","owner":"GeoBosh","description":"R package providing functions for computing Expected shortfall (ES) and Value at risk (VaR)","archived":false,"fork":false,"pushed_at":"2022-11-09T21:54:49.000Z","size":1415,"stargazers_count":6,"open_issues_count":0,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-27T03:41:45.363Z","etag":null,"topics":["expected-shortfall","locations-scale-transformations","quantile","quantile-functions","risk","value-at-risk"],"latest_commit_sha":null,"homepage":"https://geobosh.github.io/cvar/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/GeoBosh.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-04-08T10:10:34.000Z","updated_at":"2024-08-02T19:11:14.000Z","dependencies_parsed_at":"2022-08-21T06:21:01.415Z","dependency_job_id":null,"html_url":"https://github.com/GeoBosh/cvar","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoBosh%2Fcvar","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoBosh%2Fcvar/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoBosh%2Fcvar/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/GeoBosh%2Fcvar/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/GeoBosh","download_url":"https://codeload.github.com/GeoBosh/cvar/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248695477,"owners_count":21146956,"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","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":["expected-shortfall","locations-scale-transformations","quantile","quantile-functions","risk","value-at-risk"],"created_at":"2024-10-14T21:44:56.147Z","updated_at":"2025-04-13T12:43:33.036Z","avatar_url":"https://github.com/GeoBosh.png","language":"R","readme":"[![CRANStatusBadge](https://www.r-pkg.org/badges/version/cvar)](https://cran.r-project.org/package=cvar)\n[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/cvar)](https://www.r-pkg.org/pkg/cvar)\n[![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/cvar?color=blue)](https://r-pkg.org/pkg/cvar)\n[![R-CMD-check](https://github.com/GeoBosh/cvar/workflows/R-CMD-check/badge.svg)](https://github.com/GeoBosh/cvar/actions)\n[![codecov](https://codecov.io/gh/GeoBosh/cvar/branch/master/graph/badge.svg?token=2SW9HKG71Y)](https://app.codecov.io/gh/GeoBosh/cvar)\n\n \n## Overview\n\nCompute expected shortfall (ES) and Value at Risk (VaR) from a\nquantile function, distribution function, random number generator or\nprobability density function.  ES is also known as Conditional Value\nat Risk (CVaR). Virtually any continuous distribution can be\nspecified.  The functions are vectorised over the arguments.\nThe computations are done directly from the definitions, see e.g. Acerbi\nand Tasche (2002). Some support for GARCH models is provided, as well.\n\n\n## Installing cvar\n\nThe [latest stable version](https://cran.r-project.org/package=cvar) is on CRAN. \n\n    install.packages(\"cvar\")\n\nThe vignette\n[Guide_cvar]( https://CRAN.R-project.org/package=cvar/vignettes/Guide_cvar.pdf) shipping\nwith the package gives illustrative examples (can also be opened from R with\n`vignette(\"Guide_cvar\", package = \"cvar\")`).\n\nYou can install the [development version](https://github.com/GeoBosh/cvar) of `cvar` from Github:\n\n    library(devtools)\n    install_github(\"GeoBosh/cvar\")\n\n\n## Overview\n\nPackage `cvar` is a small `R` package with, essentially two\nfunctions \u0026#x2014; `ES` for computing the expected shortfall\nand `VaR` for Value at Risk.  The user specifies the\ndistribution by supplying one of the functions that define a\ncontinuous distribution\u0026#x2014;currently this can be a quantile\nfunction (qf), cumulative distribution function (cdf) or\nprobability density function (pdf). Virtually any continuous\ndistribution can be specified.\n\nThe functions are vectorised over the parameters of the\ndistributions, making bulk computations more convenient, for\nexample for forecasting or model evaluation.\n\nThe name of this package, \"cvar\", comes from *Conditional Value at\nRisk* (CVaR), which is an alternative term for expected shortfall.\n\nWe chose to use the standard names `ES` and `VaR`,\ndespite the possibility for name clashes with same named\nfunctions in other packages, rather than invent possibly\ndifficult to remember alternatives. Just call the functions as\n`cvar::ES` and `cvar::VaR` if necessary.\n\nLocations-scale transformations can be specified separately\nfrom the other distribution parameters. This is useful when\nsuch parameters are not provided directly by the distribution\nat hand. The use of these parameters often leads to more\nefficient computations and better numerical accuracy even if\nthe distribution has its own parameters for this purpose. Some\nof the examples for `VaR` and `ES` illustrate this\nfor the Gaussian distribution.\n\nSince VaR is a quantile, functions computing it for a given\ndistribution are convenience functions. `VaR` exported by\n`cvar` could be attractive in certain workflows because of\nits vectorised distribution parameters, the location-scale\ntransformation and the possibility to compute it from cdf's\nwhen quantile functions are not available.\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeobosh%2Fcvar","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgeobosh%2Fcvar","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgeobosh%2Fcvar/lists"}