Continuous Norming
Function for standardizing raking weights Raking weights get divided b...
S3 method for printing the results and regression function of a cnorm ...
Print Model Selection Information
Determine Regression Model
Compute Parameters of a Beta Binomial Distribution
Build cnorm object from data and bestModel model object
Build regression function for bestModel
Internal function for retrieving regression function coefficients at s...
Internal function for retrieving regression function coefficients at s...
Internal function for retrieving regression function coefficients at s...
Check Monotonicity of Predicted Values
Check the consistency of the norm data model
Check, if NA or values <= 0 occur and issue warning
Fit a beta-binomial regression model for continuous norming
Fit a beta binomial regression model
Fit a beta-binomial regression model for continuous norming
Cross-validation for Term Selection in cNORM
Launcher for the graphical user interface of cNORM
Continuous Norming
Compute powers of the explanatory variable a as well as of the person ...
Weighting of cases through iterative proportional fitting (Raking)
Create a table based on first order derivative of the regression model...
Derivative of regression model
Diagnostic Information for Beta-Binomial Model
Determine groups and group means
Computes the curve for a specific T value
Calculates the standard error (SE) or root mean square error (RMSE) of...
Calculate the negative log-likelihood for a beta binomial regression m...
Plot cnormBetaBinomial Model with Data and Percentile Lines
Calculate the negative log-likelihood for a beta-binomial regression m...
Prints the results and regression function of a cnorm model
Calculate Cumulative Probabilities, Density, Percentiles, and Z-Scores...
Create a norm table based on model for specific age
S3 function for plotting cnorm objects
Plot cnormBetaBinomial Model with Data and Percentile Lines
General convenience plotting function
Plot the density function per group by raw score
Plot first order derivative of regression model
Plot manifest and fitted norm scores
Plot norm curves
Plot norm curves against actual percentiles
Format raw and norm tables The function takes a raw or norm table, con...
Generates a series of plots with number curves by percentile for diffe...
Plot manifest and fitted raw scores
Evaluate information criteria for regression model
Predict Norm Scores from Raw Scores
S3 method for printing model selection information
Predict Norm Scores from Raw Scores
Predict mean and standard deviation for a beta binomial regression mod...
Predict alpha and beta parameters for a beta-binomial regression model
Retrieve norm value for raw score at a specific age
Predict raw values
Prepare data for modeling in one step (convenience method)
Check for horizontal and vertical extrapolation
Determine the norm scores of the participants in each subsample
Determine the norm scores of the participants by sliding window
Create a table with norm scores assigned to raw scores for a specific ...
Regression function
Simulate mean per age
Simulate sd per age
Simulate raw test scores based on Rasch model
Standardize a numeric vector
Summarize a Beta-Binomial Continuous Norming Model
Summarize a Beta-Binomial Continuous Norming Model
Swiftly compute Taylor regression models for distribution free continu...
Weighted Harrell-Davis quantile estimator
Weighted quantile estimator through case inflation
Weighted quantile estimator
Weighted type7 quantile estimator
Weighted rank estimation
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.
Useful links