Cognitive Diagnosis Modeling
Likelihood Ratio Test for Model Comparisons
Cognitive Diagnostic Indices based on Kullback-Leibler Information
tools:::Rd_package_title("CDM")
Utility Functions in CDM
Classification Reliability in a CDM
Extract Estimated Item Parameters and Skill Class Distribution Paramet...
Artificial Data: DINA and DINO
Several Datasets for the CDM Package
Fraction Subtraction Dataset with Different Subsets of Data and Differ...
PISA 2000 Reading Study (Chen & de la Torre, 2014)
Variance Matrix of a Nonlinear Estimator Using the Delta Method
Deterministic Classification and Joint Maximum Likelihood Estimation o...
Calculation of Equivalent Skill Classes in the DINA/DINO Model
Parameter Estimation for Mixed 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
Generalized Distance Discriminating Method
Differential Item Functioning in the GDINA Model
Estimating the Generalized DINA (GDINA) Model
Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model
General Diagnostic Model
Ideal Response Pattern
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 Univariat...
Information Criteria
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 Individ...
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
Root Mean Square Deviation (RMSD) Item Fit Statistic
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 Exp...
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 Respons...
Print Method for Objects of Class summary.din
Regularized Latent Class Analysis
Constructing a Dataset with Sequential Pseudo Items for Ordered Item R...
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
Wald Test for a Linear Hypothesis
Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>), the multiple group (polytomous) GDINA model (de la Torre, 2011, <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>) for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as Ravand and Robitzsch (2015).
Useful links