{"id":14068445,"url":"https://github.com/pbiecek/ddst","last_synced_at":"2025-04-13T23:36:59.602Z","repository":{"id":10315660,"uuid":"12442309","full_name":"pbiecek/ddst","owner":"pbiecek","description":"R package for Data driven smooth tests","archived":false,"fork":false,"pushed_at":"2023-08-20T22:03:27.000Z","size":13383,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-27T13:46:15.789Z","etag":null,"topics":["data-driven","r","smooth-test","statistics","test"],"latest_commit_sha":null,"homepage":"http://pbiecek.github.io/ddst/","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/pbiecek.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":null,"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}},"created_at":"2013-08-28T19:12:54.000Z","updated_at":"2023-08-20T22:03:31.000Z","dependencies_parsed_at":"2024-08-13T07:14:09.904Z","dependency_job_id":"d5c2d94c-4991-424f-88de-8baac5886dbd","html_url":"https://github.com/pbiecek/ddst","commit_stats":{"total_commits":61,"total_committers":1,"mean_commits":61.0,"dds":0.0,"last_synced_commit":"39506cfc0fba6aa122dfdff534334b066c793652"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pbiecek%2Fddst","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pbiecek%2Fddst/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pbiecek%2Fddst/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/pbiecek%2Fddst/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/pbiecek","download_url":"https://codeload.github.com/pbiecek/ddst/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248798973,"owners_count":21163396,"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":["data-driven","r","smooth-test","statistics","test"],"created_at":"2024-08-13T07:06:10.854Z","updated_at":"2025-04-13T23:36:59.572Z","avatar_url":"https://github.com/pbiecek.png","language":"R","funding_links":[],"categories":["R"],"sub_categories":[],"readme":"# Data Driven Smooth Tests with ddst package\n\n[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/ddst)](http://cran.r-project.org/web/packages/ddst)\n[![Total Downloads](http://cranlogs.r-pkg.org/badges/grand-total/ddst?color=orange)](http://cranlogs.r-pkg.org/badges/grand-total/ddst)\n[![Build Status](https://api.travis-ci.org/pbiecek/ddst.png)](https://travis-ci.org/pbiecek/ddst)\n\n## Overview\n\nDDST (ddst) stands for Data Driven Smooth Test (data driven smooth test). The test characterizes data-dependent choice of the number of components in a smooth test statistic.\n\nIn this package you will find two groups of selected data driven smooth tests: goodness-of-fit tests and nonparametric tests for comparing distributions.\n    \n### Data Driven Smooth Tests for Selected Goodness-of-Fit Problems\n\nThese tests were inspired by the results from: [*Data driven smooth tests for composite hypotheses* by Inglot, Kallenberg, and Ledwina (1997)](https://projecteuclid.org/euclid.aos/1069362746) and [*Towards data driven selection of a penalty function for data driven Neyman tests* by Inglot and Ledwina (2006)](https://www.sciencedirect.com/science/article/pii/S0024379505005082).\n\n\n* DDST for Uniformity - `ddst.uniform.test()`; see [*Towards data driven selection of a penalty function for data driven Neyman tests* by Inglot and Ledwina (2006)](https://www.sciencedirect.com/science/article/pii/S0024379505005082).\n* DDST for Exponentiality - `ddst.exp.test()`; see [*Data driven smooth tests for composite hypotheses: Comparison of powers* by Kallenberg and Ledwina (1997)](https://www.tandfonline.com/doi/abs/10.1080/00949659708811850).\n* DDST for Normality; Bounded Basis Functions - `ddst.normbounded.test()`; see [*Data-driven tests for a location-scale family revisited* by Janic and Ledwina (2009)](https://link.springer.com/article/10.1080/15598608.2009.10411952).\n* DDST for Normality; Unbounded Basis Functions - `ddst.normunbounded.test()`; see [*Detection of non-Gaussianity* by Ledwina and Wyłupek (2015)](https://www.tandfonline.com/doi/abs/10.1080/00949655.2014.983110?journalCode=gscs20).\n* DDST for Extreme Value Distribution - `ddst.evd.test()`; see [*Data-driven tests for a location-scale family revisited* by Janic and Ledwina (2009)](https://link.springer.com/article/10.1080/15598608.2009.10411952).\n\n\n### Nonparametric Data Driven Smooth Tests for Comparing Distributions\n\nA starting point of the constructions were the papers: [*Data driven rank test for two-sample problem* by Janic-Wróblewska and Ledwina (2000)](https://www.jstor.org/stable/4616603?seq=1#page_scan_tab_contents) and [*Towards data driven selection of a penalty function for data driven Neyman tests* by Inglot and Ledwina (2006)](https://www.sciencedirect.com/science/article/pii/S0024379505005082).\n\n* DDST for Two-Sample Problem - `ddst.twosample.test()`; see [*Data-driven k-sample tests* by Wyłupek (2010)](https://www.jstor.org/stable/40586684?seq=1).\n* DDST for k-Sample Problem - `ddst.ksample.test()`; see [*Data-driven k-sample tests* by Wyłupek (2010)](https://www.jstor.org/stable/40586684?seq=1).\n* DDST for Change-Point Problem - `ddst.changepoint.test()`; see [*Data driven rank test for the change point problem* by Antoch, Hušková, Janic and Ledwina (2008)](https://link.springer.com/article/10.1007/s00184-007-0139-2).\n* DDST for Stochastic Dominance in Two Samples - `ddst.forstochdom.test()`; see [*Nonparametric tests for stochastic ordering* by Ledwina and Wyłupek (2012) ](https://link.springer.com/article/10.1007/s11749-011-0278-7).\n* DDST Against Stochastic Dominance - `ddst.againststochdom.test()`; see [*Two-sample test against one-sided alternatives* by Ledwina and Wyłupek (2012)](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9469.2011.00787.x).\n* DDST for Upward Trend Alternatives - `ddst.upwardtrend.test()`; see [*Data-driven tests for trend* by Wyłupek (2013)](https://www.tandfonline.com/doi/abs/10.1080/03610926.2012.697967).\n* DDST for Umbrella Alternatives; Known Peak - `ddst.umbrellaknownp.test()`; see [*An automatic test for the umbrella alternatives* by Wyłupek (2016)](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12231).\n* DDST for Umbrella Alternatives; Unknown Peak - `ddst.umbrellaunknownp.test()`; see [*An automatic test for the umbrella alternatives* by Wyłupek (2016)](https://onlinelibrary.wiley.com/doi/abs/10.1111/sjos.12231).\n\nA more detailed overview is contained in [Data Driven Smooth Tests - Introductory Material](http://www.biecek.pl/R/ddst/description.pdf). Full details on the above procedures can be found in the related papers.\n\n## Installation\n\n```{r}\n# the easiest way to get ddst is to install it from CRAN:\ninstall.packages(\"ddst\")\n\n# Or the the development version from GitHub:\n# install.packages(\"devtools\")\ndevtools::install_github(\"pbiecek/ddst\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpbiecek%2Fddst","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpbiecek%2Fddst","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpbiecek%2Fddst/lists"}