Psychometric Modeling Infrastructure
Visualizing IRT Models
Anchor Methods for the Detection of Uniform DIF the Rasch Model
Anchor methods for the detection of uniform DIF in the Rasch model
Predict Methods for Item Response Models
Coercing Item Response Data
Bradley-Terry Model Fitting Function
Extract/Set Covariates
Response Curve Plots for IRT Models
Plotting Paired Comparison Data
Extract Discrimination Parameters of Item Response Models
Calculation of the Elementary Symmetric Functions and Their Derivative...
Generalized Partial Credit Model Fitting Function
Extract Guessing Parameters of Item Response Models
Information Plots for IRT Models
Extract Item Parameters of Item Response Models
Data Structure for Item Response Data
Set Labels
Multinomial Processing Tree (MPT) Model Fitting Function
Extract/Replace Measurement Scale
Parametric Logistic Model (n-PL) Fitting Function
Data Structure for Paired Comparisons
Partial Credit Model Fitting Function
Extract Person Parameters of Item Response Models
Person-Item Plots for IRT Models
Visualizing Bradley-Terry Models
Formatting Item Response Data
Formatting Paired Comparison Data
Profile Plots for IRT Models
Rasch Model Fitting Function
Region Plots for IRT Models
Simulate Data under a Generalized Partial Credit Model
Simulate Data under a Partial Credit Model
Simulate Data under a Parametric Logistic IRT Model
Simulate Data under a Rasch model
Simulate Data under a Rating Scale Model
Rating Scale Model Fitting Function
Subsetting Item Response Data
Subsetting/Reordering Paired Comparison Data
Summarizing and Visualizing Item Response Data
Extract Threshold Parameters of Item Response Models
Extract Upper Asymptote Parameters of Item Response Models
Extract Worth Parameters
Infrastructure for psychometric modeling such as data classes (for item response data and paired comparisons), basic model fitting functions (for Bradley-Terry, Rasch, parametric logistic IRT, generalized partial credit, rating scale, multinomial processing tree models), extractor functions for different types of parameters (item, person, threshold, discrimination, guessing, upper asymptotes), unified inference and visualizations, and various datasets for illustration. Intended as a common lightweight and efficient toolbox for psychometric modeling and a common building block for fitting psychometric mixture models in package "psychomix" and trees based on psychometric models in package "psychotree".