Package: cNORM 3.4.0
cNORM: Continuous Norming
A comprehensive toolkit for generating continuous test norms in psychometrics and biometrics, and analyzing model fit. The package offers both distribution-free modeling using Taylor polynomials and parametric modeling using the beta-binomial distribution. Originally developed for achievement tests, it is applicable to a wide range of mental, physical, or other test scores dependent on continuous or discrete explanatory variables. The package provides several advantages: It minimizes deviations from representativeness in subsamples, interpolates between discrete levels of explanatory variables, and significantly reduces the required sample size compared to conventional norming per age group. cNORM enables graphical and analytical evaluation of model fit, accommodates a wide range of scales including those with negative and descending values, and even supports conventional norming. It generates norm tables including confidence intervals. It also includes methods for addressing representativeness issues through Iterative Proportional Fitting.
Authors:
cNORM_3.4.0.tar.gz
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cNORM_3.4.0.tgz(r-4.4-any)cNORM_3.4.0.tgz(r-4.3-any)
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cNORM.pdf |cNORM.html✨
cNORM/json (API)
NEWS
# Install 'cNORM' in R: |
install.packages('cNORM', repos = c('https://wlenhard.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wlenhard/cnorm/issues
beta-binomialbiometricscontinuous-norminggrowth-curvenorm-scoresnorm-tablesnormalization-techniquespercentilepsychometricsregression-based-normingtaylor-series
Last updated 11 days agofrom:ca68ec87aa. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 12 2024 |
R-4.5-win | NOTE | Nov 12 2024 |
R-4.5-linux | NOTE | Nov 12 2024 |
R-4.4-win | NOTE | Nov 12 2024 |
R-4.4-mac | NOTE | Nov 12 2024 |
R-4.3-win | NOTE | Nov 12 2024 |
R-4.3-mac | NOTE | Nov 12 2024 |
Exports:bestModelbetaCoefficientsbuildCnormObjectcheckConsistencycnormcnorm.betabinomialcnorm.cvcNORM.GUIcomparecomputePowerscomputeWeightsderivationTablederivediagnostics.betabinomialgetGroupsgetNormCurvegetNormScoreSEmodelSummarynormTablenormTable.betabinomialplot.cnormBetaBinomialplotCnormplotDensityplotDerivativeplotNormplotNormCurvesplotPercentilesplotPercentileSeriesplotRawplotSubsetpredict.cnormBetaBinomialpredictNormpredictRawprepareDataprintSubsetrangeCheckrankByGrouprankBySlidingWindowrawTableregressionFunctionsimulateRaschstandardizesummary.cnormBetaBinomialtaylorSwiftweighted.quantileweighted.quantile.harrell.davisweighted.quantile.inflationweighted.quantile.type7weighted.rank
Dependencies:clicolorspacefansifarverggplot2gluegtableisobandlabelinglatticeleapslifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigR6RColorBrewerrlangscalestibbleutf8vctrsviridisLitewithr
Demonstration for Creating Continuous Norms with cNORM
Rendered fromcNORM-Demo.Rmd
usingknitr::rmarkdown
on Nov 12 2024.Last update: 2024-10-16
Started: 2018-07-24
Modelling Psychometric Data with Beta-Binomial Distributions
Rendered fromBetaBinomial.Rmd
usingknitr::rmarkdown
on Nov 12 2024.Last update: 2024-10-16
Started: 2024-07-25
Weighted Regression-Based Norming
Rendered fromWeightedRegression.Rmd
usingknitr::rmarkdown
on Nov 12 2024.Last update: 2024-08-25
Started: 2022-03-25