R: Continuous Norming

# Continuous Norming

## Documentation for package ‘cNORM’ version 3.6.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

autoselect.betabinomial | Automatic model selection for beta-binomial continuous norming via BIC
autoselect.shash | Automatic model selection for SinH-ArcSinH continuous norming via BIC
bestModel | Determine Regression Model
betaCoefficients | Compute Parameters of a Beta Binomial Distribution
buildCnormObject | Build cnorm object from data and bestModel model object
buildFunction | Build regression function for bestModel
calcPolyInL | Internal function for retrieving regression function coefficients at specific age
calcPolyInLBase2 | Internal function for retrieving regression function coefficients at specific age
CDC | BMI growth curves from age 2 to 25
checkConsistency | Check the consistency of the norm data model
checkWeights | Check, if NA or values <= 0 occur and issue warning
cnorm | Continuous Norming
cnorm.betabinomial | Fit a beta-binomial regression model for continuous norming
cnorm.betabinomial2 | Fit a beta-binomial regression model for continuous norming
cnorm.cv | Cross-validation for Term Selection in cNORM
cNORM.GUI | Launcher for the graphical user interface of cNORM for distribution free continuous norming
cNORM.GUI2 | Launch the cNORM Parametric Modeling Shiny Application
cnorm.shash | Fit a Sinh-Arcsinh (shash) Regression Model for Continuous Norming
compare | Compare Two Norm Models Visually
computePowers | Compute powers of the explanatory variable a as well as of the person location l (data preparation)
computeWeights | Weighting of cases through iterative proportional fitting (Raking)
derivationTable | Create a table based on first order derivative of the regression model for specific age
derive | Derivative of regression model
diagnostics.betabinomial | Diagnostic Information for Beta-Binomial Model
dshash | Sinh-Arcsinh (shash) Distribution
elfe | Sentence completion test from ELFE 1-6
getGroups | Determine groups and group means
getNormCurve | Computes the curve for a specific T value
getNormScoreSE | Calculates the standard error (SE) or root mean square error (RMSE) of the norm scores In case of large datasets, both results should be almost identical
modelSummary | Prints the results and regression function of a cnorm model
normTable | Create a norm table based on model for specific age
normTable.betabinomial | Calculate Cumulative Probabilities, Density, Percentiles, and Z-Scores for Beta-Binomial Distribution
normTable.shash | Calculate Norm Tables for Sinh-Arcsinh Distribution
plot.cnorm | S3 function for plotting cnorm objects
plot.cnormBetaBinomial | Plot cnormBetaBinomial Model with Data and Percentile Lines
plot.cnormBetaBinomial2 | Plot cnormBetaBinomial Model with Data and Percentile Lines
plot.cnormShash | Plot SinH-ArcSinH Model with Data and Percentile Lines
plotCnorm | General convenience plotting function
plotDensity | Plot the density function per group by raw score
plotDerivative | Plot first order derivative of regression model
plotNorm | Plot manifest and fitted norm scores
plotNormCurves | Plot norm curves
plotPercentiles | Plot norm curves against actual percentiles
plotPercentileSeries | Generates a series of plots with number curves by percentile for different models
plotRaw | Plot manifest and fitted raw scores
plotSubset | Evaluate information criteria for regression model
ppvt | Vocabulary development from 2.5 to 17
predict.cnormBetaBinomial | Predict Norm Scores from Raw Scores
predict.cnormBetaBinomial2 | Predict Norm Scores from Raw Scores
predict.cnormShash | Predict Norm Scores from Raw Scores
predictNorm | Retrieve norm value for raw score at a specific age
predictRaw | Predict raw values
prepareData | Prepare data for modeling in one step (convenience method)
print.cnorm | S3 method for printing model selection information
print.cnormShash | Print method for SinH-ArcSinH objects
printSubset | Print Model Selection Information
pshash | Sinh-Arcsinh (shash) Distribution
qshash | Sinh-Arcsinh (shash) Distribution
rangeCheck | Check for horizontal and vertical extrapolation
rankByGroup | Determine the norm scores of the participants in each subsample
rankBySlidingWindow | Determine the norm scores of the participants by sliding window
rawTable | Create a table with norm scores assigned to raw scores for a specific age based on the regression model
regressionFunction | Regression function
rshash | Sinh-Arcsinh (shash) Distribution
shash | Sinh-Arcsinh (shash) Distribution
simMean | Simulate mean per age
simSD | Simulate sd per age
simulateRasch | Simulate raw test scores based on Rasch model
standardize | Standardize a numeric vector
standardizeRakingWeights | Function for standardizing raking weights Raking weights get divided by the smallest weight. Thereby, all weights become larger or equal to 1 without changing the ratio of the weights to each other.
subsample_lm | K-fold Resampled Coefficient Estimation for Linear Regression
summary.cnorm | S3 method for printing the results and regression function of a cnorm model
summary.cnormBetaBinomial | Summarize a Beta-Binomial Continuous Norming Model
summary.cnormBetaBinomial2 | Summarize a Beta-Binomial Continuous Norming Model
summary.cnormShash | Summarize a SinH-ArcSinH Continuous Norming Model
taylorSwift | Swiftly compute Taylor regression models for distribution free continuous norming
weighted.quantile | Weighted quantile estimator
weighted.quantile.harrell.davis | Weighted Harrell-Davis quantile estimator
weighted.quantile.inflation | Weighted quantile estimator through case inflation
weighted.quantile.type7 | Weighted type7 quantile estimator
weighted.rank | Weighted rank estimation