{ "_id": "6ad620b16b811b735aabe335", "Package": "cNORM", "Title": "Continuous Norming", "Version": "3.7.0", "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 analyzes model fit. The package offers\ndistribution-free modeling using Taylor polynomials, as well as\nparametric modeling using the beta-binomial distribution (for\nbounded accuracy tests), the Conway-Maxwell-Poisson\ndistribution (for speeded tests and count data with over-,\nequi-, or under-dispersion), and the 'Sinh-Arcsinh' (SHASH)\ndistribution. Originally developed for psychological and\neducational assessment, it is applicable to a wide range of\nmental, physical, or other test scores dependent on continuous\nor discrete explanatory variables. The package minimizes\ndeviations from representativeness in subsamples, interpolates\nbetween discrete levels of explanatory variables, and\nsignificantly reduces the required sample size compared to\nconventional norming per age group. cNORM enables graphical and\nanalytical evaluation of model fit, accommodates a wide range\nof scales including those with negative and descending values,\nand supports conventional norming. It generates norm tables\nincluding confidence intervals and provides methods for\naddressing representativeness issues through Iterative\nProportional Fitting. Based on Lenhard et al. (2016)\n, Lenhard et al. (2019)\n, Lenhard and Lenhard (2021)\n, 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": "2026-10-19 09:51:50 UTC", "RemoteUrl": "https://github.com/wlenhard/cnorm", "RemoteRef": "HEAD", "RemoteSha": "130612bb5269a3c45080f553a337ec7d2cf734f8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-19 13:47:15 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 ", "_user": "wlenhard", "_type": "src", "_file": "cNORM_3.7.0.tar.gz", "_fileid": 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"page": "plotSubset", "title": "Evaluate information criteria for regression model", "concept": [ "plot" ], "topics": [ "plotSubset" ] }, { "page": "ppvt", "title": "Vocabulary development from 2.5 to 17", "concept": [ "vocabulary acquisition development receptive" ], "topics": [ "ppvt" ] }, { "page": "predict.cnormBetaBinomial", "title": "Predict Norm Scores from Raw Scores", "concept": [ "predict" ], "topics": [ "predict.cnormBetaBinomial" ] }, { "page": "predict.cnormBetaBinomial2", "title": "Predict Norm Scores from Raw Scores", "concept": [ "predict" ], "topics": [ "predict.cnormBetaBinomial2" ] }, { "page": "predict.cnormCMP", "title": "Predict Norm Scores from Raw Scores (CMP model)", "concept": [ "predict" ], "topics": [ "predict.cnormCMP" ] }, { "page": "predict.cnormShaSh", "title": "Predict Norm Scores from Raw Scores", "concept": [ "predict" ], "topics": [ "predict.cnormShash" ] }, { "page": "predictMoments", "title": "Model-Implied Distributional Moments at Specific Ages", 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