A C D E F G I L M N O P R S V W
| CDM-package | Cognitive Diagnosis Modeling |
| abs_approx | Utility Functions in 'CDM' |
| abs_approx_D1 | Utility Functions in 'CDM' |
| anova.din | Likelihood Ratio Test for Model Comparisons |
| anova.gdina | Likelihood Ratio Test for Model Comparisons |
| anova.gdm | Likelihood Ratio Test for Model Comparisons |
| anova.mcdina | Likelihood Ratio Test for Model Comparisons |
| anova.reglca | Likelihood Ratio Test for Model Comparisons |
| anova.slca | Likelihood Ratio Test for Model Comparisons |
| cat_paste | Utility Functions in 'CDM' |
| cdi.kli | Cognitive Diagnostic Indices based on Kullback-Leibler Information |
| CDM | Cognitive Diagnosis Modeling |
| CDM-utilities | Utility Functions in 'CDM' |
| cdm.est.class.accuracy | Classification Reliability in a CDM |
| cdm_attach_internal_function | Utility Functions in 'CDM' |
| cdm_calc_information_criteria | Utility Functions in 'CDM' |
| cdm_fa1 | Utility Functions in 'CDM' |
| cdm_fit_normal | Utility Functions in 'CDM' |
| cdm_matrix1 | Utility Functions in 'CDM' |
| cdm_matrix2 | Utility Functions in 'CDM' |
| cdm_matrixstring | Utility Functions in 'CDM' |
| cdm_parameter_regularization | Utility Functions in 'CDM' |
| cdm_pem_acceleration | Utility Functions in 'CDM' |
| cdm_pem_acceleration_assign_output_parameters | Utility Functions in 'CDM' |
| cdm_pem_inits | Utility Functions in 'CDM' |
| cdm_pem_inits_assign_parmlist | Utility Functions in 'CDM' |
| cdm_penalty_threshold_elnet | Utility Functions in 'CDM' |
| cdm_penalty_threshold_lasso | Utility Functions in 'CDM' |
| cdm_penalty_threshold_mcp | Utility Functions in 'CDM' |
| cdm_penalty_threshold_ridge | Utility Functions in 'CDM' |
| cdm_penalty_threshold_scad | Utility Functions in 'CDM' |
| cdm_penalty_threshold_scadL2 | Utility Functions in 'CDM' |
| cdm_penalty_threshold_tlp | Utility Functions in 'CDM' |
| cdm_penalty_values | Utility Functions in 'CDM' |
| cdm_print_summary_call | Utility Functions in 'CDM' |
| cdm_print_summary_computation_time | Utility Functions in 'CDM' |
| cdm_print_summary_data_frame | Utility Functions in 'CDM' |
| cdm_print_summary_information_criteria | Utility Functions in 'CDM' |
| CDM_rbind_fill | Utility Functions in 'CDM' |
| CDM_require_namespace | Utility Functions in 'CDM' |
| CDM_rmvnorm | Utility Functions in 'CDM' |
| coef.din | Extract Estimated Item Parameters and Skill Class Distribution Parameters |
| coef.gdina | Extract Estimated Item Parameters and Skill Class Distribution Parameters |
| coef.gdm | Extract Estimated Item Parameters and Skill Class Distribution Parameters |
| coef.IRT.jackknife | Jackknifing an Item Response Model |
| coef.mcdina | Extract Estimated Item Parameters and Skill Class Distribution Parameters |
| coef.slca | Extract Estimated Item Parameters and Skill Class Distribution Parameters |
| confint.din | Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals |
| csink | Opens and Closes a 'sink' Connection |
| Data-sim | Artificial Data: DINA and DINO |
| data.cdm | Several Datasets for the 'CDM' Package |
| data.cdm01 | Several Datasets for the 'CDM' Package |
| data.cdm02 | Several Datasets for the 'CDM' Package |
| data.cdm03 | Several Datasets for the 'CDM' Package |
| data.cdm04 | Several Datasets for the 'CDM' Package |
| data.cdm05 | Several Datasets for the 'CDM' Package |
| data.cdm06 | Several Datasets for the 'CDM' Package |
| data.cdm07 | Several Datasets for the 'CDM' Package |
| data.cdm08 | Several Datasets for the 'CDM' Package |
| data.cdm09 | Several Datasets for the 'CDM' Package |
| 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.fraction | Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices |
| data.fraction1 | Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices |
| data.fraction2 | Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices |
| data.fraction3 | Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices |
| data.fraction4 | Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices |
| 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.cc | PISA 2000 Reading Study (Chen & de la Torre, 2014) |
| data.pisa00R.ct | 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 | TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011) |
| data.timss07.G4.py | TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011) |
| data.timss07.G4.Qdomains | TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011) |
| data.timss11.G4.AUT | TIMSS 2011 Mathematics 4th Grade Austrian Students |
| data.timss11.G4.AUT.part | TIMSS 2011 Mathematics 4th Grade Austrian Students |
| data.timss11.G4.sa | TIMSS 2011 Mathematics 4th Grade Austrian Students |
| deltaMethod | Variance Matrix of a Nonlinear Estimator Using the Delta Method |
| din | Parameter Estimation for Mixed DINA/DINO Model |
| 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.validate.qmatrix | Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model |
| din_identifiability | Identifiability Conditions of the DINA Model |
| discrim.index | Discrimination Indices at Item-Attribute, Item and Test Level |
| discrim.index.din | Discrimination Indices at Item-Attribute, Item and Test Level |
| discrim.index.gdina | Discrimination Indices at Item-Attribute, Item and Test Level |
| discrim.index.mcdina | Discrimination Indices at Item-Attribute, Item and Test Level |
| entropy.lca | Test-specific and Item-specific Entropy for Latent Class Models |
| equivalent.dina | Determination of a Statistically Equivalent DINA Model |
| eval_likelihood | Evaluation of Likelihood |
| fraction.subtraction.data | Fraction Subtraction Data |
| fraction.subtraction.qmatrix | Fraction Subtraction Q-Matrix |
| gdd | Generalized Distance Discriminating Method |
| gdina | Estimating the Generalized DINA (GDINA) Model |
| gdina.dif | Differential Item Functioning in the GDINA Model |
| gdina.wald | Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model |
| 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 | Comparisons of Several Models |
| IRT.data | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.din | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.gdina | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.gdm | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.mcdina | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.reglca | S3 Method for Extracting Used Item Response Dataset |
| IRT.data.slca | S3 Method for Extracting Used Item Response Dataset |
| IRT.derivedParameters | Jackknifing an Item Response Model |
| IRT.expectedCounts | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.din | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.gdina | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.gdm | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.mcdina | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.reglca | S3 Method for Extracting Expected Counts |
| IRT.expectedCounts.slca | S3 Method for Extracting Expected Counts |
| IRT.factor.scores | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.factor.scores.din | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.factor.scores.gdina | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.factor.scores.gdm | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.factor.scores.mcdina | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.factor.scores.slca | S3 Methods for Extracting Factor Scores (Person Classifications) |
| IRT.frequencies | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT.frequencies.din | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT.frequencies.gdina | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT.frequencies.gdm | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT.frequencies.mcdina | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT.frequencies.slca | 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.irfprob.din | S3 Methods for Extracting Item Response Functions |
| IRT.irfprob.gdina | S3 Methods for Extracting Item Response Functions |
| IRT.irfprob.gdm | S3 Methods for Extracting Item Response Functions |
| IRT.irfprob.mcdina | S3 Methods for Extracting Item Response Functions |
| IRT.irfprob.reglca | S3 Methods for Extracting Item Response Functions |
| IRT.irfprob.slca | S3 Methods for Extracting Item Response Functions |
| IRT.irfprobPlot | Plot Item Response Functions |
| IRT.itemfit | S3 Methods for Computing Item Fit |
| IRT.itemfit.din | S3 Methods for Computing Item Fit |
| IRT.itemfit.gdina | S3 Methods for Computing Item Fit |
| IRT.itemfit.gdm | S3 Methods for Computing Item Fit |
| IRT.itemfit.reglca | S3 Methods for Computing Item Fit |
| IRT.itemfit.slca | S3 Methods for Computing Item Fit |
| IRT.jackknife | Jackknifing an Item Response Model |
| IRT.jackknife.gdina | Jackknifing an Item Response Model |
| IRT.likelihood | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.din | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.gdina | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.gdm | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.mcdina | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.reglca | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.likelihood.slca | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.marginal_posterior | S3 Method for Computation of Marginal Posterior Distribution |
| IRT.marginal_posterior.din | S3 Method for Computation of Marginal Posterior Distribution |
| IRT.marginal_posterior.gdina | S3 Method for Computation of Marginal Posterior Distribution |
| IRT.marginal_posterior.mcdina | S3 Method for Computation of Marginal Posterior Distribution |
| IRT.modelfit | S3 Methods for Assessing Model Fit |
| IRT.modelfit.din | S3 Methods for Assessing Model Fit |
| IRT.modelfit.gdina | S3 Methods for Assessing Model Fit |
| IRT.parameterTable | S3 Method for Extracting a Parameter Table |
| IRT.posterior | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.din | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.gdina | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.gdm | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.mcdina | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.reglca | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.posterior.slca | S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior |
| IRT.predict | Expected Values and Predicted Probabilities from Item Response Response Models |
| IRT.repDesign | Generation of a Replicate Design for 'IRT.jackknife' |
| IRT.RMSD | Root Mean Square Deviation (RMSD) Item Fit Statistic |
| IRT.se | Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals |
| IRT.se.din | Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals |
| IRT_frequencies_default | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT_frequencies_wrapper | S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals |
| IRT_RMSD_calc_rmsd | Root Mean Square Deviation (RMSD) Item Fit Statistic |
| itemfit.rmsea | RMSEA Item Fit |
| itemfit.sx2 | S-X2 Item Fit Statistic for Dichotomous Data |
| item_by_group | Create Dataset with Group-Specific Items |
| logLik.din | Extract Log-Likelihood |
| logLik.gdina | Extract Log-Likelihood |
| logLik.gdm | Extract Log-Likelihood |
| logLik.mcdina | Extract Log-Likelihood |
| logLik.reglca | Extract Log-Likelihood |
| logLik.slca | Extract Log-Likelihood |
| mcdina | Multiple Choice DINA Model |
| modelfit.cor | Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations |
| modelfit.cor.din | Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations |
| modelfit.cor2 | Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations |
| numerical_gradient | Numerical Computation of the Hessian Matrix |
| numerical_Hessian | Numerical Computation of the Hessian Matrix |
| numerical_Hessian_partial | Numerical Computation of the Hessian Matrix |
| osink | Opens and Closes a 'sink' Connection |
| personfit.appropriateness | Appropriateness Statistic for Person Fit Assessment |
| plot.din | Plot Method for Objects of Class din |
| plot.gdina | Estimating the Generalized DINA (GDINA) Model |
| plot.gdm | General Diagnostic Model |
| plot.itemfit.sx2 | S-X2 Item Fit Statistic for Dichotomous Data |
| plot.personfit.appropriateness | Appropriateness Statistic for Person Fit Assessment |
| plot.slca | Structured Latent Class Analysis (SLCA) |
| plot_item_mastery | S3 Methods for Plotting Item Probabilities |
| plot_item_mastery.din | S3 Methods for Plotting Item Probabilities |
| plot_item_mastery.gdina | S3 Methods for Plotting Item Probabilities |
| predict.din | Expected Values and Predicted Probabilities from Item Response Response Models |
| predict.gdina | Expected Values and Predicted Probabilities from Item Response Response Models |
| predict.gdm | Expected Values and Predicted Probabilities from Item Response Response Models |
| predict.mcdina | Expected Values and Predicted Probabilities from Item Response Response Models |
| predict.slca | Expected Values and Predicted Probabilities from Item Response Response Models |
| prep_data_long_format | Evaluation of Likelihood |
| print.din | Parameter Estimation for Mixed DINA/DINO Model |
| print.gdina | Estimating the Generalized DINA (GDINA) Model |
| print.gdm | General Diagnostic Model |
| print.mcdina | Multiple Choice DINA Model |
| print.slca | Structured Latent Class Analysis (SLCA) |
| print.summary.din | Print Method for Objects of Class summary.din |
| 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.dina | Artificial Data: DINA and DINO |
| sim.dino | Artificial Data: DINA and DINO |
| sim.gdina | Simulation of the GDINA model |
| sim.gdina.prepare | Simulation of the GDINA model |
| sim.qmatrix | Artificial Data: DINA and DINO |
| sim_model | Simulate an Item Response Model |
| skill.cor | Tetrachoric or Polychoric Correlations between Attributes |
| skill.polychor | Tetrachoric or Polychoric Correlations between Attributes |
| skillspace.approximation | Skill Space Approximation |
| skillspace.full | Creation of a Hierarchical Skill Space |
| skillspace.hierarchy | Creation of a Hierarchical Skill Space |
| slca | Structured Latent Class Analysis (SLCA) |
| summary.cdi.kli | Cognitive Diagnostic Indices based on Kullback-Leibler Information |
| summary.din | Summary Method for Objects of Class din |
| summary.din_identifiability | Identifiability Conditions of the DINA Model |
| summary.discrim.index | Discrimination Indices at Item-Attribute, Item and Test Level |
| summary.entropy.lca | Test-specific and Item-specific Entropy for Latent Class Models |
| summary.gdina | Estimating the Generalized DINA (GDINA) Model |
| summary.gdina.dif | Differential Item Functioning in the GDINA Model |
| summary.gdina.wald | Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model |
| summary.gdm | General Diagnostic Model |
| summary.IRT.compareModels | Comparisons of Several Models |
| summary.IRT.modelfit.din | S3 Methods for Assessing Model Fit |
| summary.IRT.modelfit.gdina | S3 Methods for Assessing Model Fit |
| summary.IRT.RMSD | Root Mean Square Deviation (RMSD) Item Fit Statistic |
| summary.itemfit.sx2 | S-X2 Item Fit Statistic for Dichotomous Data |
| summary.mcdina | Multiple Choice DINA Model |
| summary.modelfit.cor.din | Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations |
| summary.personfit.appropriateness | Appropriateness Statistic for Person Fit Assessment |
| summary.reglca | Regularized Latent Class Analysis |
| summary.slca | Structured Latent Class Analysis (SLCA) |
| summary_sink | Prints 'summary' and 'sink' Output in a File |
| vcov.din | Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals |
| vcov.IRT.jackknife | Jackknifing an Item Response Model |
| WaldTest | Wald Test for a Linear Hypothesis |