Cognitive Diagnosis Modeling
Utility Functions in 'CDM'
Artificial Data: DINA and DINO
Information Criteria
Root Mean Square Deviation (RMSD) Item Fit Statistic
Helper Function for Conducting Likelihood Ratio Tests
Individual Classification for Fitted Models
Comparisons of Several Models
S3 Method for Extracting Used Item Response Dataset
S3 Method for Extracting Expected Counts
S3 Methods for Extracting Factor Scores (Person Classifications)
S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals
S3 Methods for Extracting Item Response Functions
Plot Item Response Functions
S3 Methods for Computing Item Fit
Jackknifing an Item Response Model
S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior
S3 Method for Computation of Marginal Posterior Distribution
S3 Methods for Assessing Model Fit
S3 Method for Extracting a Parameter Table
Generation of a Replicate Design for 'IRT.jackknife'
Wald Test for a Linear Hypothesis
Likelihood Ratio Test for Model Comparisons
Cognitive Diagnostic Indices based on Kullback-Leibler Information
Classification Reliability in a CDM
Extract Estimated Item Parameters and Skill Class Distribution Parameters
Dataset Student Questionnaire
Several Datasets for the 'CDM' Package
Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010)
DTMR Fraction Data (Bradshaw et al., 2014)
Dataset ECPE
Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices
Dataset 'data.hr' (Ravand et al., 2013)
Dataset Jang (2009)
MELAB Data (Li, 2011)
Large-Scale Dataset with Multiple Groups
Dataset for Polytomous GDINA Model
PISA 2000 Reading Study (Chen & de la Torre, 2014)
Dataset SDA6 (Jurich & Bradshaw, 2014)
TIMSS 2003 Mathematics 8th Grade (Su et al., 2013)
TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011)
TIMSS 2011 Mathematics 4th Grade Austrian Students
Variance Matrix of a Nonlinear Estimator Using the Delta Method
Parameter Estimation for Mixed DINA/DINO Model
Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model
Calculation of Equivalent Skill Classes in the DINA/DINO Model
Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model
Identifiability Conditions of the DINA Model
Discrimination Indices at Item-Attribute, Item and Test Level
Test-specific and Item-specific Entropy for Latent Class Models
Determination of a Statistically Equivalent DINA Model
Evaluation of Likelihood
Fraction Subtraction Data
Fraction Subtraction Q-Matrix
Generalized Distance Discriminating Method
Estimating the Generalized DINA (GDINA) Model
Differential Item Functioning in the GDINA Model
Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model
General Diagnostic Model
Ideal Response Pattern
Create Dataset with Group-Specific Items
RMSEA Item Fit
S-X2 Item Fit Statistic for Dichotomous Data
Extract Log-Likelihood
Multiple Choice DINA Model
Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations
Numerical Computation of the Hessian Matrix
Opens and Closes a 'sink' Connection
Appropriateness Statistic for Person Fit Assessment
Plot Method for Objects of Class din
S3 Methods for Plotting Item Probabilities
Expected Values and Predicted Probabilities from Item Response Response Models
Print Method for Objects of Class summary.din
Regularized Latent Class Analysis
Constructing a Dataset with Sequential Pseudo Items for Ordered Item Responses
Data Simulation Tool for DINA, DINO and mixed DINA and DINO Data
Simulation of the GDINA model
Simulate an Item Response Model
Tetrachoric or Polychoric Correlations between Attributes
Skill Space Approximation
Creation of a Hierarchical Skill Space
Structured Latent Class Analysis (SLCA)
Summary Method for Objects of Class din
Prints 'summary' and 'sink' Output in a File
Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals