{ "_id": "6ac3dedd34f9a32edea23019", "Package": "BayesBrainMap", "Type": "Package", "Title": "Estimate Brain Networks and Connectivity with Population-Derived\nPriors", "Version": "0.3.1", "Authors@R": "c(\nperson(given = \"Amanda\",\nfamily = \"Mejia\",\nemail = \"mandy.mejia@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4312-8974\")),\nperson(given = \"Damon\",\nfamily = \"Pham\",\nemail = \"damondpham@gmail.com\",\nrole = c(\"aut\"),\ncomment = c(ORCID = \"0000-0001-7563-4727\")),\nperson(given = \"Nohelia\",\nfamily = \"Da Silva\",\nemail = \"nohelia.dasilva@gmail.com\",\nrole = c(\"ctb\"))\n)", "Maintainer": "Amanda Mejia ", "Description": "Implements Bayesian brain mapping with population-derived\npriors, including the original model described in Mejia et al.\n(2020) , the model with\nspatial priors described in Mejia et al. (2022)\n, and the model with\npopulation-derived priors on functional connectivity described\nin Mejia et al. (2025) .\nPopulation-derived priors are based on templates representing\nestablished brain network maps, for example derived from\nindependent component analysis (ICA), parcellations, or other\nmethods.  Model estimation is based on expectation-maximization\nor variational Bayes algorithms. Includes direct support for\n'CIFTI', 'GIFTI', and 'NIFTI' neuroimaging file formats.", "License": "GPL-3", "Additional_repositories": "https://inla.r-inla-download.org/R/testing", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "URL": "https://github.com/mandymejia/BayesBrainMap", "BugReports": "https://github.com/mandymejia/BayesBrainMap/issues", "Config/roxygen2/version": "8.1.0", "Repository": "https://mandymejia.r-universe.dev", "Date/Publication": "2026-10-05 14:43:03 UTC", "RemoteUrl": "https://github.com/mandymejia/bayesbrainmap", "RemoteRef": "HEAD", "RemoteSha": "468d6e18b7715dcba8a7b19401d806186d1584c4", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-05 16:40:23 UTC", "User": "root" }, "Author": "Amanda Mejia [aut, cre] (ORCID:\n),\nDamon Pham [aut] (ORCID: ),\nNohelia Da Silva [ctb]", "_user": "mandymejia", "_type": "src", "_file": "BayesBrainMap_0.3.1.tar.gz", "_fileid": "https://r2.ropensci.org/21910c28136ddc2d7c9365f7a79bc29f183de2261019b8fc346ae6d0529a5c7f", "_filesize": 369541, "_sha256": "21910c28136ddc2d7c9365f7a79bc29f183de2261019b8fc346ae6d0529a5c7f", "_expires": "2027-01-13T17:31:05.000Z", "_created": "2026-10-05T16:40:23.000Z", "_published": "2026-10-05T17:31:08.639Z", "_jobs": [ { "job": 111873393733, "time": 292, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11359756453" }, { "job": 111873393709, "time": 264, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11359073675" }, { "job": 111873393754, "time": 386, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "11361886895" }, { "job": 111873393750, "time": 358, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "11360269332" }, { "job": 111871710523, "time": 235, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "11359625622" }, { "job": 111873393813, "time": 115, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "11359321572" }, { "job": 111873393898, "time": 337, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "11359118843" }, { "job": 111873393858, "time": 330, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "OK", "artifact": "11359745925" }, { "job": 111873393767, "time": 339, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "11359138829" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412", "_status": "success", "_upstream": "https://github.com/mandymejia/bayesbrainmap", "_commit": { "id": "468d6e18b7715dcba8a7b19401d806186d1584c4", "author": "Damon Pham ", "committer": "Damon Pham ", "message": "resubmission\n", "time": 1791211383 }, "_maintainer": { "name": "Amanda Mejia", "email": "mandy.mejia@gmail.com", "login": "mandymejia", "twitter": "@mandyfmejia", "description": "Associate Professor in the Department of Statistics at Indiana University. PI of the StatMIND (Statistical Methods for Individualized Neuro-Discovery) lab.", "uuid": 5199186, "orcid": "0000-0002-4312-8974" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.6.0", "role": "Depends" }, { "package": "abind", "role": "Imports" }, { "package": "fMRItools", "version": ">= 0.8.0", "role": "Imports" }, { "package": "foreach", "role": "Imports" }, { "package": "Matrix", "role": "Imports" }, { "package": "matrixStats", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "pesel", "role": "Imports" }, { "package": "SQUAREM", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "ciftiTools", "version": ">= 0.13.2", "role": "Suggests" }, { "package": "excursions", "role": "Suggests" }, { "package": "RNifti", "role": "Suggests" }, { "package": "oro.nifti", "role": "Suggests" }, { "package": "gifti", "role": "Suggests" }, { "package": "ggplot2", "role": "Suggests" }, { "package": "parallel", "role": "Suggests" }, { "package": "doParallel", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "INLA", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "mandymejia", "_selfowned": true, "_usedby": 0, "_updates": [ { "week": "2025-40", "n": 1 }, { "week": "2025-43", "n": 1 }, { "week": "2025-45", "n": 1 }, { "week": "2026-01", "n": 1 }, { "week": "2026-03", "n": 2 }, { "week": "2026-04", "n": 1 }, { "week": "2026-05", "n": 2 }, { "week": "2026-06", "n": 8 }, { "week": "2026-07", "n": 1 }, { "week": "2026-16", "n": 1 }, { "week": "2026-17", "n": 1 }, { "week": "2026-23", "n": 1 }, { "week": "2026-28", "n": 2 }, { "week": "2026-29", "n": 3 }, { "week": "2026-31", "n": 1 }, { "week": "2026-32", "n": 5 }, { "week": "2026-34", "n": 1 }, { "week": "2026-38", "n": 26 }, { "week": "2026-39", "n": 10 }, { "week": "2026-40", "n": 1 }, { "week": "2026-41", "n": 1 } ], "_tags": [ { "name": "2.0", "date": "2026-02-06" }, { "name": "3.0", "date": "2026-10-05" } ], "_stars": 2, "_contributors": [ { "user": "damondpham", "count": 113, "uuid": 14958204 }, { "user": "diegoderman", "count": 11, "uuid": 93217934 }, { "user": "mandymejia", "count": 6, "uuid": 5199186 }, { "user": "noheliadasilvas", "count": 4, "uuid": 65466801 } ], "_userbio": { "uuid": 5199186, "type": "user", "name": "Mandy Mejia", "followers": 80, "description": "Associate Professor in the Department of Statistics at Indiana University. PI of the StatMIND (Statistical Methods for Individualized Neuro-Discovery) lab." }, "_downloads": { "count": 332, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/BayesBrainMap" }, "_devurl": "https://github.com/mandymejia/bayesbrainmap", "_searchresults": 17, "_rbuild": "4.6.1", "_assets": [ "extra/BayesBrainMap.html", "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/mandymejia/bayesbrainmap", "_realowner": "mandymejia", "_cranurl": true, "_releases": [ { "version": "0.1.3", "date": "2025-07-04" }, { "version": "0.2.0", "date": "2026-02-03" }, { "version": "0.3.1", "date": "2026-10-02" } ], "_exports": [ "BrainMap", "engagements", "estimate_prior", "export_prior", "fit_BBM", "id_engagements" ], "_help": [ { "page": "bdiag_m2", "title": "Bdiag m2", "topics": [ "bdiag_m2" ] }, { "page": "BrainMap", "title": "BrainMap", "topics": [ "BrainMap" ] }, { "page": "Chol_samp_fun", "title": "Cholesky-based FC sampling", "topics": [ "Chol_samp_fun" ] }, { "page": "EM_BBM.spatial", "title": "EM Algorithms for Bayesian brain maps", "topics": [ "EM_BBM.independent", "EM_BBM.spatial" ] }, { "page": "engagements", "title": "engagements", "topics": [ "engagements" ] }, { "page": "estimate_nu", "title": "Universally estimate IW dof parameter nu based on method of moments, so that no empirical variance is under-estimated", "topics": [ "estimate_nu" ] }, { "page": "estimate_nu_matrix", "title": "Estimate IW dof parameter nu based on method of moments", "topics": [ "estimate_nu_matrix" ] }, { "page": "estimate_prior", "title": "Estimate prior", "topics": [ "estimate_prior", "estimate_prior.cifti", "estimate_prior.gifti", "estimate_prior.nifti" ] }, { "page": "estimate.ESS", "title": "Estimation of effective sample size", "topics": [ "estimate.ESS" ] }, { "page": "export_prior", "title": "Export prior", "topics": [ "export_prior" ] }, { "page": "fit_BBM", "title": "Bayesian brain mapping", "topics": [ "fit_BBM" ] }, { "page": "id_engagements", "title": "Engagements of (spatial) Bayesian brain mapping", "topics": [ "id_engagements" ] }, { "page": "IW_var", "title": "Compute theoretical Inverse-Wishart variance of covariance matrix elements", "topics": [ "IW_var" ] }, { "page": "IW_var_cor", "title": "Compute theoretical Inverse-Wishart variance of correlation matrix elements", "topics": [ "IW_var_cor" ] }, { "page": "lik", "title": "Compute likelihood in SPDE model for ESS estimation", "topics": [ "lik" ] }, { "page": "plot.bMap_eng.cifti", "title": "Plot engagements", "topics": [ "plot.bMap_eng.cifti" ] }, { "page": "plot.bMap.cifti", "title": "Plot fit_BBM estiamte", "topics": [ "plot.bMap.cifti" ] }, { "page": "plot.bMap.matrix", "title": "Plot prior", "topics": [ "plot.bMap.matrix" ] }, { "page": "plot.bMap.nifti", "title": "Plot prior", "topics": [ "plot.bMap.nifti" ] }, { "page": "plot.prior.cifti", "title": "Plot prior", "topics": [ "plot.prior.cifti" ] }, { "page": "plot.prior.gifti", "title": "Plot prior", "topics": [ "plot.prior.gifti" ] }, { "page": "plot.prior.matrix", "title": "Plot prior", "topics": [ "plot.prior.matrix" ] }, { "page": "plot.prior.nifti", "title": "Plot prior", "topics": [ "plot.prior.nifti" ] }, { "page": "pw_estimate", "title": "Estimate residual autocorrelation for prewhitening", "topics": [ "pw_estimate" ] }, { "page": "summary.bMap_eng.cifti", "title": "Summarize a '\"bMap_eng.cifti\"' object", "topics": [ "print.bMap_eng.cifti", "print.summary.bMap_eng.cifti", "summary.bMap_eng.cifti" ] }, { "page": "summary.bMap_eng.matrix", "title": "Summarize a '\"bMap_eng.matrix\"' object", "topics": [ "print.bMap_eng.matrix", "print.summary.bMap_eng.matrix", "summary.bMap_eng.matrix" ] }, { "page": "summary.bMap_eng.nifti", "title": "Summarize a '\"bMap_eng.nifti\"' object", "topics": [ "print.bMap_eng.nifti", "print.summary.bMap_eng.nifti", "summary.bMap_eng.nifti" ] }, { "page": "summary.bMap.cifti", "title": "Summarize a '\"bMap.cifti\"' object", "topics": [ "print.bMap.cifti", "print.summary.bMap.cifti", "summary.bMap.cifti" ] }, { "page": "summary.bMap.matrix", "title": "Summarize a '\"bMap.matrix\"' object", "topics": [ "print.bMap.matrix", "print.summary.bMap.matrix", "summary.bMap.matrix" ] }, { "page": "summary.bMap.nifti", "title": "Summarize a '\"bMap.nifti\"' object", "topics": [ "print.bMap.nifti", "print.summary.bMap.nifti", "summary.bMap.nifti" ] }, { "page": "summary.prior.cifti", "title": "Summarize a '\"prior.cifti\"' object", "topics": [ "print.prior.cifti", "print.summary.prior.cifti", "summary.prior.cifti" ] }, { "page": "summary.prior.gifti", "title": "Summarize a '\"prior.gifti\"' object", "topics": [ "print.prior.gifti", "print.summary.prior.gifti", "summary.prior.gifti" ] }, { "page": "summary.prior.matrix", "title": "Summarize a '\"prior.matrix\"' object", "topics": [ "print.prior.matrix", "print.summary.prior.matrix", "summary.prior.matrix" ] }, { "page": "summary.prior.nifti", "title": "Summarize a '\"prior.nifti\"' object", "topics": [ "print.prior.nifti", "print.summary.prior.nifti", "summary.prior.nifti" ] }, { "page": "UpdateTheta_BBM", "title": "Parameter Estimates in EM Algorithm for Bayesian brain map", "topics": [ "UpdateTheta_BBM", "UpdateTheta_iBM" ] }, { "page": "var_sq_err", "title": "Compute the error between empirical and theoretical variance of covariance matrix elements", "topics": [ "var_sq_err" ] }, { "page": "var_sq_err_constrained", "title": "Compute the overall error between empirical and theoretical variance of CORRELATION matrix elements", "topics": [ "var_sq_err_constrained" ] } ], "_readme": "https://github.com/mandymejia/bayesbrainmap/raw/HEAD/README.md", "_rundeps": [ "abind", "codetools", "fMRItools", "foreach", "iterators", "lattice", "Matrix", "matrixStats", "pesel", "SQUAREM" ], "_score": 5.029789470831856, "_indexed": true, "_nocasepkg": "bayesbrainmap", "_universes": [ "mandymejia" ], "_previous": "0.2.0", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.3.1", "date": "2026-10-05T16:43:18.000Z", "distro": "resolute", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/2ffc77999fb6f101a1b87b40553723dfadf60d10819fba52ebd43d448775e613", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.6.1", "os": "linux", "version": "0.3.1", "date": "2026-10-05T16:42:45.000Z", "distro": "resolute", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/63fa258f37625ee396abe0468b982ed77fd8e764fd7f2f06e25593c418f1c191", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.5.3", "os": "mac", "version": "0.3.1", "date": "2026-10-05T17:26:21.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/a5416c42b14a8c5a91508090d7302df334ed1920c2b8b1b09c4dd5acd61be00f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.6.1", "os": "mac", "version": "0.3.1", "date": "2026-10-05T17:15:15.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/1db367a709f13a7f755f6e92d94e21477b52c3aa00cfc19f386a59f7e5c8b9c9", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.6.0", "os": "wasm", "version": "0.3.1", "date": "2026-10-05T16:42:39.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/99da7b350061f2fdbb4cb5ae08f476d36f9393bfaa0275bb018b5a88df7c0d0e", "status": "success", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.7.0", "os": "win", "version": "0.3.1", "date": "2026-10-05T16:41:52.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/6bcc93eb1b8c249d958898b70573867d92b5eb76507a1262f7eb93d49787ea5f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.5.3", "os": "win", "version": "0.3.1", "date": "2026-10-05T16:42:35.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/cef454ba9b4e39ec47dd434480e56589819f2f50114c65550a403243f0a52b39", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" }, { "r": "4.6.1", "os": "win", "version": "0.3.1", "date": "2026-10-05T16:41:50.000Z", "commit": "468d6e18b7715dcba8a7b19401d806186d1584c4", "fileid": "https://r2.ropensci.org/70ce3c6d9db8c477a104a440cf16b19e7f7f74e9c39e6b7219227ea81399847f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/mandymejia/actions/runs/37342148412" } ] }