{ "_id": "6a15b862acfb0bcc41d67e1c", "Package": "cNORM", "Title": "Continuous Norming", "Version": "3.6.0", "Authors@R": "c(\nperson(\"Alexandra\", \"Lenhard\",\nemail = \"lenhard@psychometrica.de\",\nrole = \"aut\",\ncomment = c(ORCID = \"0000-0001-8680-4381\")),\nperson(\"Wolfgang\", \"Lenhard\",\nemail = \"wolfgang.lenhard@uni-wuerzburg.de\",\nrole = c(\"cre\", \"aut\"),\ncomment = c(ORCID = \"0000-0002-8184-6889\")),\nperson(\"Sebastian\", \"Gary\",\nrole = \"aut\"),\nperson(\"WPS\", \"Publisher\",\nrole = \"fnd\",\ncomment = \"https://www.wpspublish.com/\"))", "Description": "Generates continuous test norms in psychometrics and\nbiometrics, and analyzing model fit. The package offers both\ndistribution-free modeling using Taylor polynomials and\nparametric modeling using the beta-binomial and the\n'Sinh-Arcsinh' distribution. Originally developed for\nachievement tests, it is applicable to a wide range of mental,\nphysical, or other test scores dependent on continuous or\ndiscrete explanatory variables. The package provides several\nadvantages: It minimizes deviations from representativeness in\nsubsamples, interpolates between discrete levels of explanatory\nvariables, and significantly reduces the required sample size\ncompared to conventional norming per age group. cNORM enables\ngraphical and analytical evaluation of model fit, accommodates\na wide range of scales including those with negative and\ndescending values, and as well supports conventional norming.\nIt generates norm tables including confidence intervals.\nMethods for addressing representativeness issues are available\nthrough Iterative Proportional Fitting. Based on Lenhard et\nal. (2016) , Lenhard et al.\n(2019) , Lenhard and Lenhard\n(2021) and Gary et al. (2023)\n.", "License": "AGPL-3", "URL": "https://www.psychometrica.de/cNorm_en.html,\nhttps://github.com/WLenhard/cNORM", "BugReports": "https://github.com/WLenhard/cNORM/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-US", "LazyData": "true", "LazyDataCompression": "xz", "Config/roxygen2/version": "8.0.0", "Repository": "https://wlenhard.r-universe.dev", "Date/Publication": "2026-05-26 09:05:07 UTC", "RemoteUrl": "https://github.com/wlenhard/cnorm", "RemoteRef": "HEAD", "RemoteSha": "cdf9efc9eeeaf66b2409b3776b8e36bf3a943525", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-26 15:04:19 UTC", "User": "root" }, "Author": "Alexandra Lenhard [aut] (ORCID:\n),\nWolfgang Lenhard [cre, aut] (ORCID:\n),\nSebastian Gary [aut],\nWPS Publisher [fnd] (https://www.wpspublish.com/)", "Maintainer": "Wolfgang Lenhard ", "MD5sum": "2d8a07cc91bdeb369cbf80d1c399800d", "_user": "wlenhard", "_type": "src", "_file": "cNORM_3.6.0.tar.gz", 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"cNORM-Demo.html", "title": "Demonstration for Creating Continuous Norms with cNORM", "author": "Wolfgang Lenhard, Alexandra Lenhard", "engine": "knitr::rmarkdown", "headings": [ "Introduction", "Mathematical Background", "In a nutshell: Establishing continuous norming models", "Data Preparation and modelling", "Representativeness of the data base", "Weighting", "Determining the norm sample size", "Data preparation", "Calculation of Manifest Norms and Modeling", "Model Selection", "Generating Norm Tables", "predictNorm", "predictRaw", "normTable", "rawTable", "Combining cNORM with IRT-based ability estimates", "Useful Tools", "Plot of raw scores", "Plot of norm scores", "Probability Density Function", "Curve analysis of the regression function", "References" ], "created": "2018-07-24 11:06:28", "modified": "2026-05-25 06:12:25", "commits": 50 }, { "source": "BetaBinomial.Rmd", "filename": "BetaBinomial.html", "title": "Modelling Norms with the Beta-Binomial Distribution", "author": 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