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CDM

Cognitive Diagnosis Modeling

If you use CDM and have suggestions for improvement or have found bugs, please email me at robitzsch@leibniz-ipn.de.

Manual

The manual may be found here https://alexanderrobitzsch.github.io/CDM/

CRAN version

The official version of CDM is hosted on CRAN and may be found here. The CRAN version can be installed from within R using:

utils::install.packages("CDM")

GitHub version

The version hosted here is the development version of CDM. The GitHub version can be installed using devtools as:

devtools::install_github("alexanderrobitzsch/CDM")

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Version

Install

install.packages('CDM')

Monthly Downloads

3,441

Version

7.4-19

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/alexanderrobitzsch/CDM

Homepage

https://sites.google.com/site/alexanderrobitzsch2/software

Maintainer

Alexander Robitzsch

Last Published

September 5th, 2019

Functions in CDM (7.4-19)

IRT.expectedCounts

S3 Method for Extracting Expected Counts

S3 Method for Extracting Used Item Response Dataset

Root Mean Square Deviation (RMSD) Item Fit Statistic

Helper Function for Conducting Likelihood Ratio Tests

Artificial Data: DINA and DINO

Information Criteria

S3 Methods for Extracting Item Response Functions

IRT.irfprobPlot

Plot Item Response Functions

S3 Methods for Assessing Model Fit

IRT.parameterTable

S3 Method for Extracting a Parameter Table

IRT.factor.scores

S3 Methods for Extracting Factor Scores (Person Classifications)

Individual Classification for Fitted Models

IRT.frequencies

S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals

Likelihood Ratio Test for Model Comparisons

Utility Functions in CDM

S3 Methods for Computing Item Fit

cdm.est.class.accuracy

Classification Reliability in a CDM

Extract Estimated Item Parameters and Skill Class Distribution Parameters

S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior

IRT.compareModels

Comparisons of Several Models

IRT.marginal_posterior

S3 Method for Computation of Marginal Posterior Distribution

DTMR Fraction Data (Bradshaw et al., 2014)

Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010)

Large-Scale Dataset with Multiple Groups

Cognitive Diagnostic Indices based on Kullback-Leibler Information

MELAB Data (Li, 2011)

Test-specific and Item-specific Entropy for Latent Class Models

Discrimination Indices at Item-Attribute, Item and Test Level

Dataset SDA6 (Jurich & Bradshaw, 2014)

din.deterministic

Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model

data.timss03.G8.su

TIMSS 2003 Mathematics 8th Grade (Su et al., 2013)

din.equivalent.class

Calculation of Equivalent Skill Classes in the DINA/DINO Model

Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model

Differential Item Functioning in the GDINA Model

plot_item_mastery

S3 Methods for Plotting Item Probabilities

Expected Values and Predicted Probabilities from Item Response Response Models

Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices

equivalent.dina

Determination of a Statistically Equivalent DINA Model

Create Dataset with Group-Specific Items

eval_likelihood

Evaluation of Likelihood

Jackknifing an Item Response Model

Generation of a Replicate Design for IRT.jackknife

Dataset Student Questionnaire

Wald Test for a Linear Hypothesis

Summary Method for Objects of Class din

Dataset data.hr (Ravand et al., 2013)

Dataset Jang (2009)

Several Datasets for the CDM Package

Variance Matrix of a Nonlinear Estimator Using the Delta Method

Prints summary and sink Output in a File

personfit.appropriateness

Appropriateness Statistic for Person Fit Assessment

Plot Method for Objects of Class din

Parameter Estimation for Mixed DINA/DINO Model

print.summary.din

Print Method for Objects of Class summary.din

Regularized Latent Class Analysis

data.timss07.G4.lee

TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011)

data.timss11.G4.AUT

TIMSS 2011 Mathematics 4th Grade Austrian Students

Generalized Distance Discriminating Method

Dataset for Polytomous GDINA Model

numerical_Hessian

Numerical Computation of the Hessian Matrix

Simulation of the GDINA model

Opens and Closes a sink Connection

Estimating the Generalized DINA (GDINA) Model

Simulate an Item Response Model

fraction.subtraction.data

Fraction Subtraction Data

fraction.subtraction.qmatrix

Fraction Subtraction Q-Matrix

skillspace.hierarchy

Creation of a Hierarchical Skill Space

Structured Latent Class Analysis (SLCA)

S-X2 Item Fit Statistic for Dichotomous Data

Extract Log-Likelihood

Tetrachoric or Polychoric Correlations between Attributes

PISA 2000 Reading Study (Chen & de la Torre, 2014)

din.validate.qmatrix

Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model

General Diagnostic Model

din_identifiability

Identifiability Conditions of the DINA Model

skillspace.approximation

Skill Space Approximation

Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals

ideal.response.pattern

Ideal Response Pattern

Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations

Multiple Choice DINA Model

sequential.items

Constructing a Dataset with Sequential Pseudo Items for Ordered Item Responses

Data Simulation Tool for DINA, DINO and mixed DINA and DINO Data