Test Theory Analysis and Biclustering
Item Entropy
Model Fit Functions for Items
IIF for 4PLM
Mutual Information
Number Right Score
Log-likelihood function used in the Maximization Step (M-Step).
Omega Coefficient
convert parameters to optimization target
Passage Rate of Student
Student Percentile Ranks
Item Lift
Item Odds
Generate Item Report for Non-Binary Test Data
Simple Item Statistics
Item Threshold
Item-Total Correlation
Joint Correct Response Rate
Joint Sample Size
Joint Selection Rate
Latent Class Analysis
LDparam set
Local Dependence Biclustering
Local Dependence Latent Rank Analysis
Four-Parameter Logistic Model
Long Format Data Conversion
Latent Rank Analysis
Utility function for searching DAG
Alpha Coefficient
Alpha Coefficient if Item removed
Prior distribution function with guessing parameter
Biclustering and Ranklustering Analysis
Bicluster Network Model
Biserial Correlation
Binary pattern maker
Bayesian Network Model
calc Fit Indices
Conditional Correct Response Rate
Correct Response Rate
Conditional Selection Rate
Classical Test Theory
dataFormat
Dimensionality
Generate category labels for response data
generate start values for optimize
Grid Search for Optimal Parameters
cumulative probability of GRM
Item Information Function for GRM
Probability function for GRM
Graded Response Model (GRM)
IIF for 2PLM
IIF for 3PLM
Inter-Item Analysis for Psychometric Data
Infinite Relational Model
Estimating Item parameters using EM algorithm
Item-Total Biserial Correlation
Phi-Coefficient
Plot Method for Objects of Class "exametrika"
Calculate Polychoric Correlation Likelihood
Polychoric Correlation
Polychoric Correlation Matrix
Polyserial Correlation
Print Method for Exametrika Objects
internal functions for PSD of Item parameters
bivariate normal CDF
Rasch Model
Generate Error Message for Invalid Response Type
Generate Score Report for Non-Binary Test Data
Prior distribution function with respect to the slope.
softmax function
Standardized Score
Stanine Scores
Structure Learning for BNM by simple GA
Structure Learning for BNM by PBIL
Structure Learning for LDLRA by PBIL algorithm
StudentAnalysis
Model Fit Functions for test whole
Model Fit Functions for saturated model
TIF for IRT
TRF for IRT
Simple Test Statistics
Tetrachoric Correlation
Tetrachoric Correlation Matrix
Three-Parameter Logistic Model
Two-Parameter Logistic Model
Implements comprehensive test data engineering methods as described in Shojima (2022, ISBN:978-9811699856). Provides statistical techniques for engineering and processing test data: Classical Test Theory (CTT) with reliability coefficients for continuous ability assessment; Item Response Theory (IRT) including Rasch, 2PL, and 3PL models with item/test information functions; Latent Class Analysis (LCA) for nominal clustering; Latent Rank Analysis (LRA) for ordinal clustering with automatic determination of cluster numbers; Biclustering methods including infinite relational models for simultaneous clustering of examinees and items without predefined cluster numbers; and Bayesian Network Models (BNM) for visualizing inter-item dependencies. Features local dependence analysis through LRA and biclustering, parameter estimation, dimensionality assessment, and network structure visualization for educational, psychological, and social science research.