Function reference • CDM

All functions
`anova(<din>)` `anova(<gdina>)` `anova(<gdm>)` `anova(<mcdina>)` `anova(<reglca>)` `anova(<slca>)`	Likelihood Ratio Test for Model Comparisons
`cdi.kli()` `summary(<cdi.kli>)`	Cognitive Diagnostic Indices based on Kullback-Leibler Information
`CDM-package` `CDM`	Cognitive Diagnosis Modeling
`CDM_require_namespace()` `cdm_attach_internal_function()` `cdm_print_summary_data_frame()` `cdm_print_summary_call()` `cdm_print_summary_computation_time()` `cdm_matrixstring()` `CDM_rmvnorm()` `cdm_fit_normal()` `cdm_fa1()` `CDM_rbind_fill()` `cdm_matrix2()` `cdm_matrix1()` `cdm_penalty_threshold_scad()` `cdm_penalty_threshold_lasso()` `cdm_penalty_threshold_ridge()` `cdm_penalty_threshold_elnet()` `cdm_penalty_threshold_scadL2()` `cdm_penalty_threshold_tlp()` `cdm_penalty_threshold_mcp()` `cdm_parameter_regularization()` `cdm_penalty_values()` `cdm_parameter_regularization()` `cdm_pem_inits()` `cdm_pem_inits_assign_parmlist()` `cdm_pem_acceleration()` `cdm_pem_acceleration_assign_output_parameters()` `abs_approx()` `abs_approx_D1()` `cdm_calc_information_criteria()` `cdm_print_summary_information_criteria()` `cat_paste()`	Utility Functions in CDM
`cdm.est.class.accuracy()`	Classification Reliability in a CDM
`coef(<din>)` `coef(<gdina>)` `coef(<mcdina>)` `coef(<gdm>)` `coef(<slca>)`	Extract Estimated Item Parameters and Skill Class Distribution Parameters
`sim.dina` `sim.dino` `sim.qmatrix`	Artificial Data: DINA and DINO
`data.cdm01` `data.cdm02` `data.cdm03` `data.cdm04` `data.cdm05` `data.cdm06` `data.cdm07` `data.cdm08` `data.cdm09` `data.cdm10`	Several Datasets for the CDM Package
`data.dcm`	Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010)
`data.dtmr`	DTMR Fraction Data (Bradshaw et al., 2014)
`data.ecpe`	Dataset ECPE
`data.fraction1` `data.fraction2` `data.fraction3` `data.fraction4` `data.fraction5`	Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices
`data.hr`	Dataset `data.hr` (Ravand et al., 2013)
`data.jang`	Dataset Jang (2009)
`data.melab`	MELAB Data (Li, 2011)
`data.mg`	Large-Scale Dataset with Multiple Groups
`data.pgdina`	Dataset for Polytomous GDINA Model
`data.pisa00R.ct` `data.pisa00R.cc`	PISA 2000 Reading Study (Chen & de la Torre, 2014)
`data.sda6`	Dataset SDA6 (Jurich & Bradshaw, 2014)
`data.Students`	Dataset Student Questionnaire
`data.timss03.G8.su`	TIMSS 2003 Mathematics 8th Grade (Su et al., 2013)
`data.timss07.G4.lee` `data.timss07.G4.py` `data.timss07.G4.Qdomains`	TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011)
`data.timss11.G4.AUT` `data.timss11.G4.AUT.part` `data.timss11.G4.sa`	TIMSS 2011 Mathematics 4th Grade Austrian Students
`deltaMethod()`	Variance Matrix of a Nonlinear Estimator Using the Delta Method
`din.deterministic()`	Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model
`din.equivalent.class()`	Calculation of Equivalent Skill Classes in the DINA/DINO Model
`din()` `print(<din>)`	Parameter Estimation for Mixed DINA/DINO Model
`din.validate.qmatrix()`	Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model
`din_identifiability()` `summary(<din_identifiability>)`	Identifiability Conditions of the DINA Model
`discrim.index()` `summary(<discrim.index>)`	Discrimination Indices at Item-Attribute, Item and Test Level
`entropy.lca()` `summary(<entropy.lca>)`	Test-specific and Item-specific Entropy for Latent Class Models
`equivalent.dina()`	Determination of a Statistically Equivalent DINA Model
`eval_likelihood()` `prep_data_long_format()`	Evaluation of Likelihood
`fraction.subtraction.data`	Fraction Subtraction Data
`fraction.subtraction.qmatrix`	Fraction Subtraction Q-Matrix
`gdd()`	Generalized Distance Discriminating Method
`gdina.dif()` `summary(<gdina.dif>)`	Differential Item Functioning in the GDINA Model
`gdina()` `summary(<gdina>)` `plot(<gdina>)` `print(<gdina>)`	Estimating the Generalized DINA (GDINA) Model
`gdina.wald()` `summary(<gdina.wald>)`	Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model
`gdm()` `summary(<gdm>)` `print(<gdm>)` `plot(<gdm>)`	General Diagnostic Model
`ideal.response.pattern()`	Ideal Response Pattern
`IRT.anova()`	Helper Function for Conducting Likelihood Ratio Tests
`IRT.classify()`	Individual Classification for Fitted Models
`IRT.compareModels()` `summary(<IRT.compareModels>)`	Comparisons of Several Models
`IRT.data()`	S3 Method for Extracting Used Item Response Dataset
`IRT.expectedCounts()`	S3 Method for Extracting Expected Counts
`IRT.factor.scores()`	S3 Methods for Extracting Factor Scores (Person Classifications)
`IRT.frequencies()` `IRT_frequencies_default()` `IRT_frequencies_wrapper()`	S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals
`IRT.IC()`	Information Criteria
`IRT.irfprob()`	S3 Methods for Extracting Item Response Functions
`IRT.irfprobPlot()`	Plot Item Response Functions
`IRT.itemfit()`	S3 Methods for Computing Item Fit
`IRT.jackknife()` `IRT.derivedParameters()` `coef(<IRT.jackknife>)` `vcov(<IRT.jackknife>)`	Jackknifing an Item Response Model
`IRT.likelihood()` `IRT.posterior()`	S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior
`IRT.marginal_posterior()`	S3 Method for Computation of Marginal Posterior Distribution
`IRT.modelfit()` `summary(<IRT.modelfit.din>)` `summary(<IRT.modelfit.gdina>)`	S3 Methods for Assessing Model Fit
`IRT.parameterTable()`	S3 Method for Extracting a Parameter Table
`IRT.repDesign()`	Generation of a Replicate Design for `IRT.jackknife`
`IRT.RMSD()` `summary(<IRT.RMSD>)` `IRT_RMSD_calc_rmsd()`	Root Mean Square Deviation (RMSD) Item Fit Statistic
`itemfit.rmsea()`	RMSEA Item Fit
`itemfit.sx2()` `summary(<itemfit.sx2>)` `plot(<itemfit.sx2>)`	S-X2 Item Fit Statistic for Dichotomous Data
`item_by_group()`	Create Dataset with Group-Specific Items
`logLik(<din>)` `logLik(<gdina>)` `logLik(<mcdina>)` `logLik(<gdm>)` `logLik(<slca>)` `logLik(<reglca>)`	Extract Log-Likelihood
`mcdina()` `summary(<mcdina>)` `print(<mcdina>)`	Multiple Choice DINA Model
`modelfit.cor()` `modelfit.cor2()` `modelfit.cor.din()` `summary(<modelfit.cor.din>)`	Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations
`numerical_Hessian()` `numerical_Hessian_partial()` `numerical_gradient()`	Numerical Computation of the Hessian Matrix
`osink()` `csink()`	Opens and Closes a `sink` Connection
`personfit.appropriateness()` `summary(<personfit.appropriateness>)` `plot(<personfit.appropriateness>)`	Appropriateness Statistic for Person Fit Assessment
`plot(<din>)`	Plot Method for Objects of Class din
`plot_item_mastery()`	S3 Methods for Plotting Item Probabilities
`IRT.predict()` `predict(<din>)` `predict(<gdina>)` `predict(<mcdina>)` `predict(<gdm>)` `predict(<slca>)`	Expected Values and Predicted Probabilities from Item Response Response Models
`print(<summary.din>)`	Print Method for Objects of Class summary.din
`reglca()` `summary(<reglca>)`	Regularized Latent Class Analysis
`sequential.items()`	Constructing a Dataset with Sequential Pseudo Items for Ordered Item Responses
`sim.din()`	Data Simulation Tool for DINA, DINO and mixed DINA and DINO Data
`sim.gdina()` `sim.gdina.prepare()`	Simulation of the GDINA model
`sim_model()`	Simulate an Item Response Model
`skill.cor()` `skill.polychor()`	Tetrachoric or Polychoric Correlations between Attributes
`skillspace.approximation()`	Skill Space Approximation
`skillspace.hierarchy()` `skillspace.full()`	Creation of a Hierarchical Skill Space
`slca()` `summary(<slca>)` `print(<slca>)` `plot(<slca>)`	Structured Latent Class Analysis (SLCA)
`summary(<din>)`	Summary Method for Objects of Class din
`summary_sink()`	Prints `summary` and `sink` Output in a File
`vcov(<din>)` `confint(<din>)` `IRT.se()`	Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals
`WaldTest()`	Wald Test for a Linear Hypothesis

Reference

All functions