{ "_id": "6a13ee02acfb0bcc41d2f5aa", "Type": "Package", "Package": "konfound", "Title": "Quantify the Robustness of Causal Inferences", "Version": "1.0.4", "Authors@R": "c(\nperson(c(\"Joshua\", \"M\"), \"Rosenberg\", , \"jmrosen48@gmail.com\", role = c(\"aut\", \"cre\")),\nperson(\"Ran\", \"Xu\", , \"ranxu@msu.edu\", role = \"ctb\"),\nperson(\"Qinyun\", \"Lin\", , \"linqinyun1001@gmail.com\", role = \"ctb\"),\nperson(\"Spiro\", \"Maroulis\", , \"Spiro.Maroulis@asu.edu\", role = \"ctb\"),\nperson(\"Sarah\", \"Narvaiz\", , \"snarvaiz@alum.utk.edu\", role = \"ctb\"),\nperson(c(\"Kenneth\", \"A\"), \"Frank\", , \"kenfrank@msu.edu\", role = \"ctb\"),\nperson(\"Wei\", \"Wang\", , \"wwang93@vols.utk.edu\", role = \"ctb\"),\nperson(\"Yunhe\", \"Cui\", , \"yunhe.cui@uconn.edu\", role = \"ctb\"),\nperson(\"Gaofei\", \"Zhang\", , \"gaofei.2.zhang@uconn.edu\", role = \"ctb\"),\nperson(\"Xuesen\", \"Cheng\", , \"chengxu3@msu.edu\", role = \"ctb\"),\nperson(\"JiHoon\", \"Choi\", , \"choijih3@msu.edu\", role = \"ctb\"),\nperson(\"Guan\", \"Saw\", , \"guan.saw@cgu.edu\", role = \"ctb\")\n)", "Description": "Statistical methods that quantify the conditions necessary\nto alter inferences, also known as sensitivity analysis, are\nbecoming increasingly important to a variety of quantitative\nsciences. A series of recent works, including Frank (2000)\n and Frank et al. (2013)\n extend previous sensitivity\nanalyses by considering the characteristics of omitted\nvariables or unobserved cases that would change an inference if\nsuch variables or cases were observed. These analyses generate\nstatements such as \"an omitted variable would have to be\ncorrelated at xx with the predictor of interest (e.g., the\ntreatment) and outcome to invalidate an inference of a\ntreatment effect\". Or \"one would have to replace pp percent of\nthe observed data with nor which the treatment had no effect to\ninvalidate the inference\". We implement these recent\ndevelopments of sensitivity analysis and provide modules to\ncalculate these two robustness indices and generate such\nstatements in R. In particular, the functions konfound(),\npkonfound() and mkonfound() allow users to calculate the\nrobustness of inferences for a user's own model, a single\npublished study and multiple studies respectively.", "License": "MIT + file LICENSE", "URL": "https://github.com/konfound-project/konfound,\nhttps://konfound-it.org/konfound/", "BugReports": "https://github.com/konfound-project/konfound/issues", "VignetteBuilder": "knitr", "Encoding": "UTF-8", "LazyData": "true", "RoxygenNote": "7.3.3", "Roxygen": "list(roclets = c(\"collate\", \"rd\", \"namespace\",\n\"doctest::dt_roclet\"), packages = \"doctest\")", "Config/pak/sysreqs": "cmake make libicu-dev libjpeg-dev libpng-dev\nlibxml2-dev libssl-dev", "Repository": "https://konfound-project.r-universe.dev", "Date/Publication": "2026-04-02 17:22:28 UTC", "RemoteUrl": "https://github.com/konfound-project/konfound", "RemoteRef": "HEAD", "RemoteSha": "f3543a699d6ca26ede25ddcf74f8419e273d3d6e", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-25 06:32:03 UTC", "User": "root" }, "Author": "Joshua M Rosenberg [aut, cre],\nRan Xu [ctb],\nQinyun Lin [ctb],\nSpiro Maroulis [ctb],\nSarah Narvaiz [ctb],\nKenneth A Frank [ctb],\nWei Wang [ctb],\nYunhe Cui [ctb],\nGaofei Zhang [ctb],\nXuesen Cheng [ctb],\nJiHoon Choi [ctb],\nGuan Saw [ctb]", "Maintainer": "Joshua M Rosenberg ", "MD5sum": "1fda98226739fab11248456c30541901", "_user": "konfound-project", "_type": "src", "_file": "konfound_1.0.4.tar.gz", "_fileid": "f27c53e6896bfcf7e1a5c03b78ab11d8983b7db5c4146719c2e0db4acba12ac7", "_filesize": 351118, "_sha256": "f27c53e6896bfcf7e1a5c03b78ab11d8983b7db5c4146719c2e0db4acba12ac7", "_created": "2026-05-25T06:32:03.000Z", "_published": "2026-05-25T06:36:50.918Z", "_distro": "noble", "_jobs": [ { "job": 77667365645, "time": 238, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "7192956675" }, { "job": 77667365671, "time": 230, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": 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"_upstream": "https://github.com/konfound-project/konfound", "_commit": { "id": "f3543a699d6ca26ede25ddcf74f8419e273d3d6e", "author": "Jihoon Choi <50298939+JihoonChoi26@users.noreply.github.com>", "committer": "GitHub ", "message": "Merge pull request #127 from konfound-project/vam-update\n\nVAM: update pi for peer effect to be pi_p", "time": 1775150548 }, "_maintainer": { "name": "Joshua M Rosenberg", "email": "jmrosen48@gmail.com", "login": "jrosen48", "description": "Associate Professor at the University of Tennessee, Knoxville", "uuid": 4596214 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "broom", "role": "Imports" }, { "package": "broom.mixed", "role": "Imports" }, { "package": "crayon", "role": "Imports" }, { "package": "dplyr", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "lavaan", "role": "Imports" }, { "package": "purrr", "role": "Imports" }, { "package": "rlang", 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"topics": [ "binary_dummy_data" ] }, { "page": "cal_delta_star", "title": "Calculate delta star for sensitivity analysis", "topics": [ "cal_delta_star" ] }, { "page": "cal_rxy", "title": "Calculate rxy based on ryxGz, rxz, and ryz", "topics": [ "cal_rxy" ] }, { "page": "cal_rxz", "title": "Calculate R2xz based on variances and standard error", "topics": [ "cal_rxz" ] }, { "page": "cal_ryz", "title": "Calculate R2yz based on ryxGz and R2", "topics": [ "cal_ryz" ] }, { "page": "chisq_p", "title": "Perform a Chi-Square Test", "topics": [ "chisq_p" ] }, { "page": "concord1", "title": "Concord1 data", "topics": [ "concord1" ] }, { "page": "get_kr_df", "title": "Extract Degrees of Freedom for Fixed Effects in a Linear Mixed-Effects Model", "topics": [ "get_kr_df" ] }, { "page": "konfound", "title": "Konfound Analysis for Various Model Types", "topics": [ "konfound" ] }, { "page": "konfound_glm", "title": "Konfound Analysis for Generalized Linear Models", "topics": [ "konfound_glm" ] }, { "page": "konfound_glm_dichotomous", "title": "Konfound Analysis for Generalized Linear Models with Dichotomous Outcomes", "topics": [ "konfound_glm_dichotomous" ] }, { "page": "konfound_lm", "title": "Konfound Analysis for Linear Models", "topics": [ "konfound_lm" ] }, { "page": "konfound_lmer", "title": "Konfound Analysis for Linear Mixed-Effects Models", "topics": [ "konfound_lmer" ] }, { "page": "mkonfound", "title": "Meta-Analysis and Sensitivity Analysis for Multiple Studies", "topics": [ "mkonfound" ] }, { "page": "mkonfound_ex", "title": "Example data for the mkonfound function", "topics": [ "mkonfound_ex" ] }, { "page": "output_df", "title": "Output data frame based on model estimates and thresholds", "topics": [ "output_df" ] }, { "page": "output_print", "title": "Output printed text with formatting", "topics": [ "output_print" ] }, { "page": "output_table", "title": "Output a Tidy Table from a Model Object", "topics": [ "output_table" ] }, { "page": "pkonfound", "title": 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