{ "_id": "6a0f66daacfb0bcc41c5d596", "Package": "CGNM", "Type": "Package", "Title": "Cluster Gauss-Newton Method", "Version": "0.9.3", "Author": "Yasunori Aoki [aut, cre]", "Authors@R": "person(given = \"Yasunori\",\nfamily = \"Aoki\",\nrole = c(\"aut\", \"cre\"),\nemail = \"yaoki@uwaterloo.ca\")", "Maintainer": "Yasunori Aoki ", "Description": "Find multiple solutions of a nonlinear least squares\nproblem. Cluster Gauss-Newton method does not assume\nuniqueness of the solution of the nonlinear least squares\nproblem and compute multiple minimizers. Please cite the\nfollowing paper when this software is used in your research:\nAoki et al. (2020) . Cluster\nGauss–Newton method. Optimization and Engineering, 1-31. Please\ncite the following paper when profile likelihood plot is drawn\nwith this software and used in your research: Aoki and Sugiyama\n(2024) . Cluster Gauss-Newton method\nfor a quick approximation of profile likelihood: With\napplication to physiologically-based pharmacokinetic models.\nCPT Pharmacometrics Syst Pharmacol.13(1):54-67. GPT based\nhelper bot available at\n", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "RoxygenNote": "7.3.2", "VignetteBuilder": "knitr", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-21 09:42:22 UTC", "User": "root" }, "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-01-08 15:40:07 UTC", "RemoteUrl": "https://github.com/cran/CGNM", "RemoteRef": "HEAD", "RemoteSha": "f8acd800f24e630d0036a89c32ced4fed2caa0a5", "MD5sum": "48ebfc32f50bbdbde573bef6cd08e3f7", "_user": "cran", "_type": "src", "_file": "CGNM_0.9.3.tar.gz", "_fileid": "d6f778d931135dbbcc3435cfc6c316c594f8a2fc6e80217e13f60bde4afc595d", "_filesize": 811732, "_sha256": "d6f778d931135dbbcc3435cfc6c316c594f8a2fc6e80217e13f60bde4afc595d", "_created": "2026-05-21T09:42:22.000Z", "_published": "2026-05-21T20:11:06.079Z", "_distro": "noble", "_jobs": [ { "job": 77259372533, "time": 151, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7132474474" }, { "job": 77259372531, "time": 162, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7132477378" }, { "job": 77259372172, "time": 290, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7132423189" }, { "job": 77259371987, "time": 110, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7146103398" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/26218056196", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/CGNM", "_commit": { "id": "f8acd800f24e630d0036a89c32ced4fed2caa0a5", "author": "Yasunori Aoki ", "committer": "cran-robot ", "message": "version 0.9.3\n", "time": 1767886807 }, "_maintainer": { "name": "Yasunori Aoki", "email": "yaoki@uwaterloo.ca" }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "stats", "role": "Imports" }, { "package": "ggplot2", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "shiny", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2025-37", "n": 1 }, { "week": "2026-02", "n": 1 } ], "_tags": [ { "name": "0.9.2", "date": "2025-09-11" }, { "name": "0.9.3", "date": "2026-01-08" } ], "_stars": 0, "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 697, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/CGNM" }, "_searchresults": 7, "_rbuild": "4.6.0", "_assets": [ "extra/CGNM.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "manual.pdf" ], "_realowner": "cran", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2022-01-06" }, { "version": "0.1.1", "date": "2022-01-10" }, { "version": "0.3.1", "date": "2022-03-14" }, { "version": "0.3.2", "date": "2022-06-04" }, { "version": "0.4.0", "date": "2022-07-13" }, { "version": "0.5.1", "date": "2022-11-01" }, { "version": "0.6.2", "date": "2023-01-14" }, { "version": "0.6.5", "date": "2023-05-19" }, { "version": "0.6.7", "date": "2024-01-18" }, { "version": "0.8.0", "date": "2024-04-12" }, { "version": "0.9.0", "date": "2024-06-14" }, { "version": "0.9.1", "date": "2025-01-28" }, { "version": "0.9.2", "date": "2025-09-11" }, { "version": "0.9.3", "date": "2026-01-08" } ], "_exports": [ "acceptedApproximateMinimizers", "acceptedIndices", "acceptedIndices_binary", "acceptedMaxSSR", "bestApproximateMinimizers", "Cluster_Gauss_Newton_Bootstrap_method", "Cluster_Gauss_Newton_EBE_method", "Cluster_Gauss_Newton_method", "col_quantile", "compare_profileLikelihood", "make_ShinyCGNM_doseData", "make_ShinyCGNM_observationData", "plot_2DprofileLikelihood", "plot_goodnessOfFit", "plot_paraDistribution_byHistogram", "plot_paraDistribution_byViolinPlots", "plot_parameterValue_scatterPlots", "plot_profileLikelihood", "plot_Rank_SSR", "plot_simulationMatrixWithCI", "plot_simulationWithCI", "plot_SSR_parameterValue", "plot_SSRsurface", "shinyCGNM", "suggestInitialLowerRange", "suggestInitialUpperRange", "table_parameterSummary", "table_profileLikelihoodConfidenceInterval", "topIndices" ], "_help": [ { "page": "acceptedApproximateMinimizers", "title": "acceptedApproximateMinimizers", "topics": [ "acceptedApproximateMinimizers" ] }, { "page": "acceptedIndices", "title": "acceptedIndices", "topics": [ "acceptedIndices" ] }, { "page": "acceptedIndices_binary", "title": "acceptedIndices_binary", "topics": [ "acceptedIndices_binary" ] }, { "page": "acceptedMaxSSR", "title": "acceptedMaxSSR", "topics": [ "acceptedMaxSSR" ] }, { "page": "bestApproximateMinimizers", "title": "bestApproximateMinimizers", "topics": [ "bestApproximateMinimizers" ] }, { "page": "Cluster_Gauss_Newton_Bootstrap_method", "title": "Cluster_Gauss_Newton_Bootstrap_method", "topics": [ "Cluster_Gauss_Newton_Bootstrap_method" ] }, { "page": "Cluster_Gauss_Newton_EBE_method", "title": "Cluster_Gauss_Newton_EBE_method", "topics": [ "Cluster_Gauss_Newton_EBE_method" ] }, { "page": "Cluster_Gauss_Newton_method", "title": "Cluster_Gauss_Newton_method", "topics": [ "Cluster_Gauss_Newton_method" ] }, { "page": "col_quantile", "title": "col_quantile", "topics": [ "col_quantile" ] }, { "page": "compare_profileLikelihood", "title": "compare_profileLikelihood", "topics": [ "compare_profileLikelihood" ] }, { "page": "make_ShinyCGNM_doseData", "title": "make_ShinyCGNM_doseData", "topics": [ "make_ShinyCGNM_doseData" ] }, { "page": "make_ShinyCGNM_observationData", "title": "make_ShinyCGNM_observationData", "topics": [ "make_ShinyCGNM_observationData" ] }, { "page": "plot_2DprofileLikelihood", "title": "plot_2DprofileLikelihood", "topics": [ "plot_2DprofileLikelihood" ] }, { "page": "plot_goodnessOfFit", "title": "plot_goodnessOfFit", "topics": [ "plot_goodnessOfFit" ] }, { "page": "plot_paraDistribution_byHistogram", "title": "plot_paraDistribution_byHistogram", "topics": [ "plot_paraDistribution_byHistogram" ] }, { "page": "plot_paraDistribution_byViolinPlots", "title": "plot_paraDistribution_byViolinPlots", "topics": [ "plot_paraDistribution_byViolinPlots" ] }, { "page": "plot_parameterValue_scatterPlots", "title": "plot_parameterValue_scatterPlots", "topics": [ "plot_parameterValue_scatterPlots" ] }, { "page": "plot_profileLikelihood", "title": "plot_profileLikelihood", "topics": [ "plot_profileLikelihood" ] }, { "page": "plot_Rank_SSR", "title": "plot_Rank_SSR", "topics": [ "plot_Rank_SSR" ] }, { "page": "plot_simulationMatrixWithCI", "title": "plot_simulationMatrixWithCI", "topics": [ "plot_simulationMatrixWithCI" ] }, { "page": "plot_simulationWithCI", "title": "plot_simulationWithCI", "topics": [ "plot_simulationWithCI" ] }, { "page": "plot_SSR_parameterValue", "title": "plot_SSR_parameterValue", "topics": [ "plot_SSR_parameterValue" ] }, { "page": "plot_SSRsurface", "title": "plot_SSRsurface", "topics": [ "plot_SSRsurface" ] }, { "page": "shinyCGNM", "title": "shinyCGNM", "topics": [ "shinyCGNM" ] }, { "page": "suggestInitialLowerRange", "title": "suggestInitialLowerRange", "topics": [ "suggestInitialLowerRange" ] }, { "page": "suggestInitialUpperRange", "title": "suggestInitialUpperRange", "topics": [ "suggestInitialUpperRange" ] }, { "page": "table_parameterSummary", "title": "table_parameterSummary", "topics": [ "table_parameterSummary" ] }, { "page": "table_profileLikelihoodConfidenceInterval", "title": "table_profileLikelihoodConfidenceInterval", "topics": [ "table_profileLikelihoodConfidenceInterval" ] }, { "page": "topIndices", "title": "topIndices", "topics": [ "topIndices" ] } ], "_rundeps": [ "base64enc", "bslib", "cachem", "cli", "commonmark", "cpp11", "digest", "farver", "fastmap", "fontawesome", "fs", "ggplot2", "glue", "gtable", "htmltools", "httpuv", "isoband", "jquerylib", "jsonlite", "labeling", "later", "lifecycle", "magrittr", "MASS", "memoise", "mime", "otel", "promises", "R6", "rappdirs", "RColorBrewer", "Rcpp", "rlang", "S7", "sass", "scales", "shiny", "sourcetools", "vctrs", "viridisLite", "withr", "xtable" ], "_vignettes": [ { "source": "CGNM-vignette.Rmd", "filename": "CGNM-vignette.html", "title": "CGNM: Cluster Gauss-Newton Method", "engine": "knitr::rmarkdown", "headings": [ "When and when not to use CGNM", "Use CGNM", "Not to use CGNM", "How to use CGNM", "Prepare the model ($\\boldsymbol f$)", "Prepare the data ($\\boldsymbol y^*$)", "Run Cluster_Gauss_Newton_method", "Obtain the approximate minimizers", "Can run residual resampling bootstrap analyses using CGNM as well", "Visualize the CGNM modelfit analysis result", "Inspect the distribution of SSR of approximate minimizers found by CGNM", "visually inspect goodness of fit of top 50 approximate minimizers", "plot model prediction with uncertainties based on residual resampling bootstrap analysis", "plot profile likelihood", "plot profile likelihood surface", "Parallel computation", "an example of parallel implementation for Mac using parallel package", "an example of parallel implementation for Windows using foreach and doParllel packages", "What is CGNM?", "The mathematical problem CGNM solves" ], "created": "2022-03-14 18:30:06", "modified": "2024-06-13 02:41:59", "commits": 7 } ], "_score": 2.3010299956639813, "_indexed": true, "_nocasepkg": "cgnm", "_universes": [ "cran" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.9.3", "date": "2026-05-21T09:44:30.000Z", "distro": "noble", "commit": "f8acd800f24e630d0036a89c32ced4fed2caa0a5", "fileid": "abec34376355bd8a133dab480a0a5a8b5e48bc6ce2ccf0df545e0fe537c36456", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/cran/actions/runs/26218056196" }, { "r": "4.6.0", "os": "linux", "version": "0.9.3", "date": "2026-05-21T09:44:36.000Z", "distro": "noble", "commit": "f8acd800f24e630d0036a89c32ced4fed2caa0a5", "fileid": "7e588b366230cdb63bec9b0dd8b31c4731fac97061dd2c010c26718ef5afe598", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/cran/actions/runs/26218056196" }, { "r": "4.6.0", "os": "wasm", "version": "0.9.3", "date": "2026-05-21T20:10:46.000Z", "commit": "f8acd800f24e630d0036a89c32ced4fed2caa0a5", "fileid": "969e04efcd3833035b4fca37bac216b7157ec2558cb908e88a754a823a9775a0", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/26218056196" } ] }