{"id":35172175,"url":"https://github.com/seungjae2525/gsamu","last_synced_at":"2026-05-17T20:37:22.477Z","repository":{"id":227836693,"uuid":"772449029","full_name":"seungjae2525/GSAMU","owner":"seungjae2525","description":"GSAMU: Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes","archived":false,"fork":false,"pushed_at":"2024-08-08T06:46:49.000Z","size":119,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-08-08T10:29:00.109Z","etag":null,"topics":["multiple-exposures","non-gaussian","r","sensitivity-analysis","time-to-event","unmeasured-confounding"],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/seungjae2525.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2024-03-15T08:04:02.000Z","updated_at":"2024-08-08T06:46:53.000Z","dependencies_parsed_at":"2024-03-15T12:02:55.229Z","dependency_job_id":"3aa8abf1-0120-439b-9a02-ad0492bda3b0","html_url":"https://github.com/seungjae2525/GSAMU","commit_stats":null,"previous_names":["seungjae2525/gsamu"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/seungjae2525/GSAMU","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seungjae2525%2FGSAMU","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seungjae2525%2FGSAMU/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seungjae2525%2FGSAMU/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seungjae2525%2FGSAMU/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/seungjae2525","download_url":"https://codeload.github.com/seungjae2525/GSAMU/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/seungjae2525%2FGSAMU/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28104109,"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":"2025-12-28T02:00:05.685Z","response_time":62,"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":["multiple-exposures","non-gaussian","r","sensitivity-analysis","time-to-event","unmeasured-confounding"],"created_at":"2025-12-28T21:02:42.090Z","updated_at":"2025-12-28T21:03:39.704Z","avatar_url":"https://github.com/seungjae2525.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, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# GSAMU\n\n\u003c!-- badges: start --\u003e\n[![Project Status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active/)\n[![Package version](https://img.shields.io/badge/GitHub-1.0.0-orange.svg)](https://github.com/seungjae2525/GSAMU/)\n[![minimal R version](https://img.shields.io/badge/R-v4.1.0+-blue.svg)](https://cran.r-project.org/)\n[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)\n[![R-CMD-check](https://github.com/seungjae2525/GSAMU/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/seungjae2525/GSAMU/actions/workflows/R-CMD-check.yaml)\n\u003c!-- badges: end --\u003e\n\n## Description\nThis is the source code for the `GSAMU` package in R. \n`GSAMU` is a package aimed at providing a novel sensitivity model to investigate the effect of correlated multiple exposures on the Gaussian, non-Gaussian, and time-to-event outcome variables.\nGiven a user-specified sensitivity parameters, the sensitivity interval is calculated. See reference for details.\n\n## Reference\nLee S, Jeong B, Lee D, Lee W (2024): Sensitivity analysis for effects of multiple exposures in the presence of unmeasured confounding: non-Gaussian and time-to-event outcomes. submitted.\n\n## Installation\n### Current GitHub release:\nInstallation using R package `remotes`:\n\n```r\nif (!require(\"remotes\", quietly=TRUE)) install.packages(\"remotes\") # if remotes not already installed\nremotes::install_github(\"seungjae2525/GSAMU\")\nlibrary(GSAMU)\n```\n\n## Example\n\nThis is a basic example which shows you how to solve a common problem:\n\n```{r example, eval=FALSE}\nlibrary(GSAMU)\n### basic example code\n## Set the bound of correlations\nbound \u003c- c(## Lower bound\n           # L1   L2   L3\n           0.01, 0.0, 0.0,\n           # X1, X2\n           rep(-0.19, 2),\n           # X3, X4\n           rep(-0.19, 2),\n\n           ## Upper bound\n           # L1   L2    L3\n           0.32, 0.5, 0.29,\n           # X1, X2\n           rep(0.15, 2),\n           # X3, X4\n           rep(0.10, 2))\n\n## For count outcome\ncontinuous.re \u003c- GSAMU(data=dataset, \n                       outcome=\"Y\", outcome.type=\"continuous\", \n                       link=\"identity\", hazard.model=NULL, \n                       confounder=c(\"L1\", \"L2\", \"L3\"),\n                       exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n                       delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n                       bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(continuous.re)\n\n## For count outcome\ncount.re \u003c- GSAMU(data=dataset, \n                  outcome=\"Y\", outcome.type=\"count\", \n                  link=\"log\", hazard.model=NULL, \n                  confounder=c(\"L1\", \"L2\", \"L3\"),\n                  exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n                  delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n                  bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(count.re)\n\n## For binary outcome with logit link\nbinary.re1 \u003c- GSAMU(data=dataset, \n                    outcome=\"Y\", outcome.type=\"count\", \n                    link=\"log\", hazard.model=NULL, \n                    confounder=c(\"L1\", \"L2\", \"L3\"),\n                    exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n                    delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n                    bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(binary.re1)\n\n## For binary outcome with logit link\nbinary.re2 \u003c- GSAMU(data=dataset, \n                    outcome=\"Y\", outcome.type=\"count\", \n                    link=\"log\", hazard.model=NULL, \n                    confounder=c(\"L1\", \"L2\", \"L3\"),\n                    exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n                    delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n                    bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(binary.re2)\n\n## For time-to-event outcome with the cox PH model\ncox.re \u003c- GSAMU(data=dataset, \n                outcome=c(\"time\", \"status\"), outcome.type=\"timetoevent\", \n                link=NULL, hazard.model=\"coxph\", \n                confounder=c(\"L1\", \"L2\", \"L3\"),\n                exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n                delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n                bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(cox.re)\n\n## For time-to-event outcome with the additive hazard model\nah.re \u003c- GSAMU(data=dataset, \n               outcome=c(\"time\", \"status\"), outcome.type=\"timetoevent\", \n               link=NULL, hazard.model=\"ah\", \n               confounder=c(\"L1\", \"L2\", \"L3\"),\n               exposure=c(\"X1\", \"X2\", \"X3\", \"X4\"),\n               delta=c(0.11, 0.22, 0.33, 0.44), bound=bound,\n               bootsCI=FALSE, B=1000, seed=231111, verbose=TRUE)\nprint(ah.re)\n```\n\nYou can also resulted plots, for example:\n\n```{r pressure, eval=FALSE}\nautoplot(object=continuous.re, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n\nautoplot(object=count.re, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n\nautoplot(object=binary.re1, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n\nautoplot(object=binary.re2, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n\nautoplot(object=cox.re, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n\nautoplot(object=ah.re, point.size=2.75, width.SI=1.55, width.CI=0.6,\n         axis.title.x.size=15, axis.text.size=16, legend.text.size=15,\n         myxlim=c(-0.25, 2))\n```\n\n## Bug Reports:\nYou can also report bugs on GitHub under [Issues](https://github.com/seungjae2525/GSAMU/issues/).\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseungjae2525%2Fgsamu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fseungjae2525%2Fgsamu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fseungjae2525%2Fgsamu/lists"}