{"id":23102634,"url":"https://github.com/dataxujing/ln0scis","last_synced_at":"2025-10-12T15:43:15.381Z","repository":{"id":112305604,"uuid":"118554018","full_name":"DataXujing/LN0SCIs","owner":"DataXujing","description":"A R and Python Packages https://pypi.org/project/LN0SCIs/ and https://cran.r-project.org/web/packages/LN0SCIs/index.html","archived":false,"fork":false,"pushed_at":"2018-12-09T04:36:41.000Z","size":116,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-10-12T15:43:14.999Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://cran.r-project.org/web/packages/LN0SCIs/vignettes/LN0SCIs-tutorial.html","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/DataXujing.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-01-23T03:44:45.000Z","updated_at":"2018-12-09T04:39:39.000Z","dependencies_parsed_at":"2023-05-12T18:15:36.174Z","dependency_job_id":null,"html_url":"https://github.com/DataXujing/LN0SCIs","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/DataXujing/LN0SCIs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataXujing%2FLN0SCIs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataXujing%2FLN0SCIs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataXujing%2FLN0SCIs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataXujing%2FLN0SCIs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DataXujing","download_url":"https://codeload.github.com/DataXujing/LN0SCIs/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DataXujing%2FLN0SCIs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279011855,"owners_count":26085005,"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":"2025-10-12T02:00:06.719Z","response_time":53,"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":[],"created_at":"2024-12-17T00:00:07.026Z","updated_at":"2025-10-12T15:43:15.364Z","avatar_url":"https://github.com/DataXujing.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\r\n[![CRAN](https://img.shields.io/cran/v/devtools.svg)]( https://CRAN.R-project.org/package=LN0SCIs )\r\n[![PyPI](https://img.shields.io/pypi/v/nine.svg)](https://pypi.python.org/pypi/LN0SCIs)\r\n# LN0SCIs   \u003ca href=\"https://github.com/DataXujing/\"\u003e\u003cimg src=\"pic/log.png\" align=\"right\" alt=\"logo\" height=\"200\" width=\"350\" /\u003e\u003c/a\u003e\r\n\r\n\r\n## Introduction\r\n\r\nThis R package based on the paper of Simultaneous Confidence Intervals for Ratios of Means of Log-normal Populations with Zeros by Xu et al. It provides serveral methods for construct simultaneous confidence intervals for ratios of means of log-normal populations with excess zeros. At last, we select 4 excellent methods which based on generalized pivotal quantity with order statistics and two-step MOVER intervals.\r\nFor the convenience of use, we make a R package called `LN0SCIs`, and it also has a python version package: \u003chttps://pypi.python.org/pypi/LN0SCIs\u003e. You can use `vignettes('LN0SCIs-tutorial')` in R  to read the tototial.\r\n\r\n\r\n## Methods\r\n\r\nWe provaide four main functions in our LN0SCIs packages, FGW(),FGH(),MOVERW() and MOVERH(), if you want to  deep understanding these four methods,you can read our paper: Simultaneous Confidence Intervals for Ratios of Means of Log-normal Populations with Zeros. the code we trust in GitHub. If you want to know how to realize them,you can read the source code.\r\n\r\n\r\n## Example\r\n\r\n+ FGW()\r\n\r\n```r\r\nlibrary(LN0SCIs)\r\n\r\n# params setting\r\np\u003c-c(0.1,0.15,0.1,0.6)\r\nn\u003c-c(30,15,10,50)\r\nmu\u003c-c(1,1.3,2,0)\r\nsigma\u003c-c(1,1,1,2)\r\nC2 \u003c- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))\r\n\r\nN\u003c-1000\r\nFGW(n,p,mu,sigma,N,C2 = C2) #base function\r\n```\r\n\r\n```r\r\n[1] \"====================Method: FGW=====================\"\r\n[1] \"The Simultaneous Confidence Intervals are:          \"\r\n               [LCL,UCL]\r\n1   [-1.235113,2.848869]\r\n2   [-0.441577,7.030192]\r\n3   [-3.57776,-2.108937]\r\n4    [-1.86122,6.480295]\r\n5  [-5.843864,-1.640372]\r\n6 [-10.008059,-2.477454]\r\n[1] \"**********************Time**************************\"\r\nTime difference of 53.041 secs\r\n```\r\n+ FGH()\r\n\r\n\r\n```r\r\n\r\np\u003c-c(0.1,0.15,0.1,0.6)\r\nn\u003c-c(30,15,10,50)\r\nmu\u003c-c(1,1.3,2,0)\r\nsigma\u003c-c(1,1,1,2)\r\nC2 \u003c- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))\r\n\r\nN\u003c-1000;\r\nFGH(n,p,mu,sigma,N,C2 = C2)\r\n```\r\n\r\n```r\r\n[1] \"====================Method: FGH=====================\"\r\n[1] \"The Simultaneous Confidence Intervals are:          \"\r\n             [LCL,UCL]\r\n1  [-0.996117,1.07758]\r\n2  [-0.36159,2.378184]\r\n3  [-2.87464,1.273622]\r\n4  [-0.302924,2.22018]\r\n5 [-2.954485,1.098756]\r\n6 [-4.086911,0.345388]\r\n[1] \"**********************Time**************************\"\r\nTime difference of 16.244 secs\r\n```\r\n\r\n+ MOVERW()\r\n\r\n```r\r\np \u003c- c(0.1,0.15,0.1,0.6)\r\nn \u003c- c(30,15,10,50)\r\nmu \u003c- c(1,1.3,2,0)\r\nsigma \u003c- c(1,1,1,2)\r\nC2 \u003c- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))\r\nN \u003c- 1000\r\n\r\nMOVERW(n,p,mu,sigma,N,C2 = C2)\r\n```\r\n\r\n```r\r\n[1] \"====================Method: MOVERW==================\"\r\n[1] \"The Simultaneous Confidence Intervals are:          \"\r\n             [LCL,UCL]\r\n1 [-1.351672,0.720717]\r\n2  [-1.11909,2.371603]\r\n3 [-1.882617,2.617392]\r\n4  [-0.86023,2.700871]\r\n5 [-1.618718,2.936072]\r\n6 [-2.967078,2.541986]\r\n[1] \"**********************Time**************************\"\r\nTime difference of 42.577 secs\r\n```\r\n\r\n+ MOVERH()\r\n\r\n```r\r\np\u003c-c(0.1,0.15,0.1,0.6)\r\nn\u003c-c(30,15,10,50)\r\nmu\u003c-c(1,1.3,2,0)\r\nsigma\u003c-c(1,1,1,2)\r\nC2 \u003c- rbind(c(-1,1,0,0),c(-1,0,1,0),c(-1,0,0,1),c(0,-1,1,0),c(0,-1,0,1),c(0,0,-1,1))\r\n\r\nN\u003c-1000;\r\nMOVERH(n,p,mu,sigma,N,C2 = C2)\r\n```\r\n\r\n```r\r\n[1] \"====================Method: MOVERH==================\"\r\n[1] \"The Simultaneous Confidence Intervals are:          \"\r\n             [LCL,UCL]\r\n1 [-1.334835,1.683172]\r\n2 [-0.806956,2.953145]\r\n3 [-2.683212,1.739062]\r\n4 [-1.130242,2.909716]\r\n5 [-2.979693,1.684558]\r\n6 [-4.145004,1.117301]\r\n[1] \"**********************Time**************************\"\r\nTime difference of 11.152 secs\r\n```\r\n\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdataxujing%2Fln0scis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdataxujing%2Fln0scis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdataxujing%2Fln0scis/lists"}