{ "_id": "6a202ca8cd65a98ecbd0b5a0", "Package": "MBESS", "Type": "Package", "Title": "The MBESS R Package", "Version": "5.0.1", "Date": "2026-06-03", "Authors@R": "c(person(\"Ken\", \"Kelley\", role=c(\"aut\", \"cre\"), email=\"kkelley@nd.edu\"))", "Maintainer": "Ken Kelley ", "Encoding": "UTF-8", "Description": "Implements methods that are useful in designing research\nstudies and analyzing data, with particular emphasis on methods\nthat are developed for or used within the behavioral,\neducational, and social sciences (broadly defined). That being\nsaid, many of the methods implemented within MBESS are\napplicable to a wide variety of disciplines. MBESS has a suite\nof functions for a variety of related topics, such as effect\nsizes, confidence intervals for effect sizes (including\nstandardized effect sizes and noncentral effect sizes), sample\nsize planning (from the accuracy in parameter estimation\n[AIPE], power analytic, equivalence, and minimum-risk point\nestimation perspectives), mediation analysis, various\nproperties of distributions, and a variety of utility\nfunctions. MBESS (pronounced 'em-bes') was originally an\nacronym for 'Methods for the Behavioral, Educational, and\nSocial Sciences,' but MBESS became more general and now\ncontains methods applicable and used in a wide variety of\nfields and is an orphan acronym, in the sense that what was an\nacronym is now literally its name. MBESS has greatly benefited\nfrom the contributions of many people over the years, who are\nacknowledged in the package documentation.", "License": "GPL-2 | GPL-3", "URL": "https://kenkelley.org/r-packages/", "RoxygenNote": "7.3.2", "Config/pak/sysreqs": "cmake make", "Repository": "https://yelleknek.r-universe.dev", "Date/Publication": "2026-06-03 12:15:57 UTC", "RemoteUrl": "https://github.com/yelleknek/mbess", "RemoteRef": "HEAD", "RemoteSha": "4e5e86e2299bf1973c03323d64e3406420fce2de", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 13:23:43 UTC", "User": "root" }, "Author": "Ken Kelley [aut, cre]", "MD5sum": "ab5d4a672fff16df905dcab127b9388b", "_user": "yelleknek", "_type": "src", "_file": "MBESS_5.0.1.tar.gz", "_fileid": "e69bdb25aa466612e895f407d9ccf28fc523ba1092ba21f0e9ae072d0b89e247", "_filesize": 858245, "_sha256": "e69bdb25aa466612e895f407d9ccf28fc523ba1092ba21f0e9ae072d0b89e247", "_created": "2026-06-03T13:23:43.000Z", "_published": "2026-06-03T13:31:20.078Z", "_distro": "noble", "_jobs": [ { "job": 79304544782, "time": 173, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7385899326" }, { "job": 79304544894, "time": 156, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7385893081" }, { "job": 79304544769, "time": 127, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7385904922" }, { "job": 79304544818, "time": 100, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7385987959" }, { "job": 79303913331, "time": 184, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7385827148" }, { "job": 79304545103, "time": 128, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7385881652" }, { "job": 79304544812, "time": 127, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7385881621" }, { "job": 79304544854, "time": 137, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7385886193" }, { "job": 79304544986, "time": 139, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7385886491" } ], "_buildurl": "https://github.com/r-universe/yelleknek/actions/runs/26887518963", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/yelleknek/mbess", "_commit": { "id": "4e5e86e2299bf1973c03323d64e3406420fce2de", "author": "Ken Kelley ", "committer": "Ken Kelley ", "message": "Fix frequency^(2m) scaling and cubic constant in ss.aipe.pcm\n\nRestore the frequency^(2m) factor in the single-subject sampling\nvariance of the change coefficient, which had been omitted. AIPE sample\nsizes are now correct for designs measured more than once per unit of\ntime (frequency > 1); results for frequency = 1, including the published\nKelley and Rausch (2011) examples (278 and 316), are unchanged.\n\nAlso correct the cubic constant K_3 from 1/1000800 to 1/100800, and\nrecompute the t critical value as the sample size iterates in the\nassurance branch rather than reusing the expected-width value.\n\nDocument the frequency^(2m) relationship and add the Raudenbush and Liu\n(2001) reference. Set version to 5.0.1.\n", "time": 1780488957 }, "_maintainer": { "name": "Ken Kelley", "email": "kkelley@nd.edu", "login": "yelleknek", "description": "Methodologist. \nDesign / Measure / Analyze", "uuid": 12966942 }, "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 3.5.0", "role": "Depends" }, { "package": "stats", "role": "Depends" }, { "package": "boot", "role": "Imports" }, { "package": "lavaan", "role": "Imports" }, { "package": "MASS", "role": "Imports" }, { "package": "methods", "role": "Imports" }, { "package": "mnormt", "role": "Imports" }, { "package": "nlme", "role": "Imports" }, { "package": "OpenMx", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "sem", "role": "Imports" }, { "package": "semTools", "role": "Imports" }, { "package": "gsl", "role": "Suggests" } ], "_owner": "yelleknek", "_selfowned": true, "_usedby": 18, "_updates": [ { "week": "2026-22", "n": 1 }, { "week": "2026-23", "n": 4 } ], "_tags": [ { "name": 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(AIPE) perspective", "topics": [ "ss.aipe.c.ancova" ] }, { "page": "ss.aipe.c.ancova.sensitivity", "title": "Sensitivity analysis for sample size planning for the (unstandardized) contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) Perspective", "topics": [ "ss.aipe.c.ancova.sensitivity" ] }, { "page": "ssAIPECRD", "title": "Find target sample sizes for the accuracy in unstandardized conditions means estimation in CRD", "topics": [ "ss.aipe.crd.both.fixedbudget", "ss.aipe.crd.both.fixedwidth", "ss.aipe.crd.nclus.fixedbudget", "ss.aipe.crd.nclus.fixedwidth", "ss.aipe.crd.nindiv.fixedbudget", "ss.aipe.crd.nindiv.fixedwidth" ] }, { "page": "ssAIPECRDES", "title": "Find target sample sizes for the accuracy in standardized conditions means estimation in CRD", "topics": [ "ss.aipe.crd.es.both.fixedbudget", "ss.aipe.crd.es.both.fixedwidth", "ss.aipe.crd.es.nclus.fixedbudget", "ss.aipe.crd.es.nclus.fixedwidth", "ss.aipe.crd.es.nindiv.fixedbudget", 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}, { "page": "ss.aipe.rc", "title": "Sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest", "topics": [ "ss.aipe.rc" ] }, { "page": "ss.aipe.rc.sensitivity", "title": "Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient", "topics": [ "ss.aipe.rc.sensitivity" ] }, { "page": "ss.aipe.reg.coef", "title": "Sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest", "topics": [ "ss.aipe.reg.coef" ] }, { "page": "ss.aipe.reg.coef.sensitivity", "title": "Sensitivity analysis for sample size planning from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficient", "topics": [ "ss.aipe.reg.coef.sensitivity" ] }, { "page": "ss.aipe.reliability", "title": "Sample Size Planning for Accuracy in Parameter Estimation for 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