Package: CDM 8.3-7
CDM: Cognitive Diagnosis Modeling
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).
Authors:
CDM_8.3-7.tar.gz
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CDM.pdf |CDM.html✨
CDM/json (API)
NEWS
| # Install 'CDM' in R: |
| install.packages('CDM', repos = c('https://alexanderrobitzsch.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alexanderrobitzsch/cdm/issues
- data.Students - Dataset Student Questionnaire
- 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
- data.dtmr - DTMR Fraction Data
- data.ecpe - Dataset ECPE
- 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'
- data.jang - Dataset Jang
- data.melab - MELAB Data
- data.mg - Large-Scale Dataset with Multiple Groups
- data.pgdina - Dataset for Polytomous GDINA Model
- data.pisa00R.cc - PISA 2000 Reading Study
- data.pisa00R.ct - PISA 2000 Reading Study
- data.sda6 - Dataset SDA6
- data.timss03.G8.su - TIMSS 2003 Mathematics 8th Grade
- data.timss07.G4.Qdomains - TIMSS 2007 Mathematics 4th Grade
- data.timss07.G4.lee - TIMSS 2007 Mathematics 4th Grade
- data.timss07.G4.py - TIMSS 2007 Mathematics 4th Grade
- 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
- fraction.subtraction.data - Fraction Subtraction Data
- fraction.subtraction.qmatrix - Fraction Subtraction Q-Matrix
- sim.dina - Artificial Data: DINA and DINO
- sim.dino - Artificial Data: DINA and DINO
- sim.qmatrix - Artificial Data: DINA and DINO
cognitive-diagnostic-modelsitem-response-theory
Last updated 5 months agofrom:da0acfeb90. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 22 2024 |
| R-4.5-win-x86_64 | OK | Nov 22 2024 |
| R-4.5-linux-x86_64 | OK | Nov 22 2024 |
| R-4.4-win-x86_64 | OK | Nov 22 2024 |
| R-4.4-mac-x86_64 | OK | Nov 22 2024 |
| R-4.4-mac-aarch64 | OK | Nov 22 2024 |
| R-4.3-win-x86_64 | OK | Nov 22 2024 |
| R-4.3-mac-x86_64 | OK | Nov 22 2024 |
| R-4.3-mac-aarch64 | OK | Nov 22 2024 |
Exports:abs_approxabs_approx_D1cat_pastecdi.klicdm_attach_internal_functioncdm_calc_information_criteriacdm_fa1cdm_fit_normalcdm_matrix1cdm_matrix2cdm_matrixstringcdm_parameter_regularizationcdm_pem_accelerationcdm_pem_acceleration_assign_output_parameterscdm_pem_initscdm_pem_inits_assign_parmlistcdm_penalty_threshold_elnetcdm_penalty_threshold_mcpcdm_penalty_threshold_ridgecdm_penalty_threshold_scadcdm_penalty_threshold_scadL2cdm_penalty_threshold_tlpcdm_penalty_valuescdm_print_summary_callcdm_print_summary_computation_timecdm_print_summary_data_framecdm_print_summary_information_criteriaCDM_rbind_fillCDM_require_namespaceCDM_rmvnormcdm.est.class.accuracycsinkdeltaMethoddindin_identifiabilitydin.deterministicdin.equivalent.classdin.validate.qmatrixdiscrim.indexentropy.lcaequivalent.dinaeval_likelihoodgddgdinagdina.difgdina.waldgdmideal.response.patternIRT_frequencies_defaultIRT_frequencies_wrapperIRT_RMSD_calc_rmsdIRT.anovaIRT.classifyIRT.compareModelsIRT.dataIRT.derivedParametersIRT.expectedCountsIRT.factor.scoresIRT.frequenciesIRT.ICIRT.irfprobIRT.irfprobPlotIRT.itemfitIRT.jackknifeIRT.likelihoodIRT.marginal_posteriorIRT.modelfitIRT.parameterTableIRT.posteriorIRT.predictIRT.repDesignIRT.RMSDIRT.seitem_by_groupitemfit.rmseaitemfit.sx2mcdinamodelfit.cormodelfit.cor.dinmodelfit.cor2numerical_gradientnumerical_Hessiannumerical_Hessian_partialosinkpersonfit.appropriatenessplot_item_masteryreglcasequential.itemssim_modelsim.dinsim.gdinasim.gdina.prepareskill.corskill.polychorskillspace.approximationskillspace.fullskillspace.hierarchyslcasummary_sinkWaldTest
Dependencies:admisclatticeMatrixmvtnormpolycorRcppRcppArmadillo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Cognitive Diagnosis Modeling | CDM-package CDM |
| Likelihood Ratio Test for Model Comparisons | anova.din anova.gdina anova.gdm anova.mcdina anova.reglca anova.slca |
| Cognitive Diagnostic Indices based on Kullback-Leibler Information | cdi.kli summary.cdi.kli |
| Utility Functions in 'CDM' | abs_approx abs_approx_D1 cat_paste CDM-utilities cdm_attach_internal_function cdm_calc_information_criteria cdm_fa1 cdm_fit_normal cdm_matrix1 cdm_matrix2 cdm_matrixstring cdm_parameter_regularization cdm_pem_acceleration cdm_pem_acceleration_assign_output_parameters cdm_pem_inits cdm_pem_inits_assign_parmlist cdm_penalty_threshold_elnet cdm_penalty_threshold_lasso cdm_penalty_threshold_mcp cdm_penalty_threshold_ridge cdm_penalty_threshold_scad cdm_penalty_threshold_scadL2 cdm_penalty_threshold_tlp cdm_penalty_values cdm_print_summary_call cdm_print_summary_computation_time cdm_print_summary_data_frame cdm_print_summary_information_criteria CDM_rbind_fill CDM_require_namespace CDM_rmvnorm |
| Classification Reliability in a CDM | cdm.est.class.accuracy |
| Extract Estimated Item Parameters and Skill Class Distribution Parameters | coef.din coef.gdina coef.gdm coef.mcdina coef.slca |
| Artificial Data: DINA and DINO | Data-sim sim.dina sim.dino sim.qmatrix |
| Several Datasets for the 'CDM' Package | data.cdm data.cdm01 data.cdm02 data.cdm03 data.cdm04 data.cdm05 data.cdm06 data.cdm07 data.cdm08 data.cdm09 data.cdm10 |
| Dataset from Book 'Diagnostic Measurement' of Rupp, Templin and Henson (2010) | data.dcm |
| DTMR Fraction Data (Bradshaw et al., 2014) | data.dtmr |
| Dataset ECPE | data.ecpe |
| Fraction Subtraction Dataset with Different Subsets of Data and Different Q-Matrices | data.fraction data.fraction1 data.fraction2 data.fraction3 data.fraction4 data.fraction5 |
| Dataset 'data.hr' (Ravand et al., 2013) | data.hr |
| Dataset Jang (2009) | data.jang |
| MELAB Data (Li, 2011) | data.melab |
| Large-Scale Dataset with Multiple Groups | data.mg |
| Dataset for Polytomous GDINA Model | data.pgdina |
| PISA 2000 Reading Study (Chen & de la Torre, 2014) | data.pisa00R.cc data.pisa00R.ct |
| Dataset SDA6 (Jurich & Bradshaw, 2014) | data.sda6 |
| Dataset Student Questionnaire | data.Students |
| TIMSS 2003 Mathematics 8th Grade (Su et al., 2013) | data.timss03.G8.su |
| TIMSS 2007 Mathematics 4th Grade (Lee et al., 2011) | data.timss07.G4.lee data.timss07.G4.py data.timss07.G4.Qdomains |
| TIMSS 2011 Mathematics 4th Grade Austrian Students | data.timss11.G4.AUT data.timss11.G4.AUT.part data.timss11.G4.sa |
| Variance Matrix of a Nonlinear Estimator Using the Delta Method | deltaMethod |
| Parameter Estimation for Mixed DINA/DINO Model | din print.din |
| Identifiability Conditions of the DINA Model | din_identifiability summary.din_identifiability |
| Deterministic Classification and Joint Maximum Likelihood Estimation of the Mixed DINA/DINO Model | din.deterministic |
| Calculation of Equivalent Skill Classes in the DINA/DINO Model | din.equivalent.class |
| Q-Matrix Validation (Q-Matrix Modification) for Mixed DINA/DINO Model | din.validate.qmatrix |
| Discrimination Indices at Item-Attribute, Item and Test Level | discrim.index discrim.index.din discrim.index.gdina discrim.index.mcdina summary.discrim.index |
| Test-specific and Item-specific Entropy for Latent Class Models | entropy.lca summary.entropy.lca |
| Determination of a Statistically Equivalent DINA Model | equivalent.dina |
| Evaluation of Likelihood | eval_likelihood prep_data_long_format |
| Fraction Subtraction Data | fraction.subtraction.data |
| Fraction Subtraction Q-Matrix | fraction.subtraction.qmatrix |
| Generalized Distance Discriminating Method | gdd |
| Estimating the Generalized DINA (GDINA) Model | gdina plot.gdina print.gdina summary.gdina |
| Differential Item Functioning in the GDINA Model | gdina.dif summary.gdina.dif |
| Wald Statistic for Item Fit of the DINA and ACDM Rule for GDINA Model | gdina.wald summary.gdina.wald |
| General Diagnostic Model | gdm plot.gdm print.gdm summary.gdm |
| Ideal Response Pattern | ideal.response.pattern |
| Helper Function for Conducting Likelihood Ratio Tests | IRT.anova |
| Individual Classification for Fitted Models | IRT.classify |
| Comparisons of Several Models | IRT.compareModels summary.IRT.compareModels |
| S3 Method for Extracting Used Item Response Dataset | IRT.data IRT.data.din IRT.data.gdina IRT.data.gdm IRT.data.mcdina IRT.data.reglca IRT.data.slca |
| S3 Method for Extracting Expected Counts | IRT.expectedCounts IRT.expectedCounts.din IRT.expectedCounts.gdina IRT.expectedCounts.gdm IRT.expectedCounts.mcdina IRT.expectedCounts.reglca IRT.expectedCounts.slca |
| S3 Methods for Extracting Factor Scores (Person Classifications) | IRT.factor.scores IRT.factor.scores.din IRT.factor.scores.gdina IRT.factor.scores.gdm IRT.factor.scores.mcdina IRT.factor.scores.slca |
| S3 Method for Computing Observed and Expected Frequencies of Univariate and Bivariate Marginals | IRT.frequencies IRT.frequencies.din IRT.frequencies.gdina IRT.frequencies.gdm IRT.frequencies.mcdina IRT.frequencies.slca IRT_frequencies_default IRT_frequencies_wrapper |
| Information Criteria | IRT.IC |
| S3 Methods for Extracting Item Response Functions | IRT.irfprob IRT.irfprob.din IRT.irfprob.gdina IRT.irfprob.gdm IRT.irfprob.mcdina IRT.irfprob.reglca IRT.irfprob.slca |
| Plot Item Response Functions | IRT.irfprobPlot |
| S3 Methods for Computing Item Fit | IRT.itemfit IRT.itemfit.din IRT.itemfit.gdina IRT.itemfit.gdm IRT.itemfit.reglca IRT.itemfit.slca |
| Jackknifing an Item Response Model | coef.IRT.jackknife IRT.derivedParameters IRT.jackknife IRT.jackknife.gdina vcov.IRT.jackknife |
| S3 Methods for Extracting of the Individual Likelihood and the Individual Posterior | IRT.likelihood IRT.likelihood.din IRT.likelihood.gdina IRT.likelihood.gdm IRT.likelihood.mcdina IRT.likelihood.reglca IRT.likelihood.slca IRT.posterior IRT.posterior.din IRT.posterior.gdina IRT.posterior.gdm IRT.posterior.mcdina IRT.posterior.reglca IRT.posterior.slca |
| S3 Method for Computation of Marginal Posterior Distribution | IRT.marginal_posterior IRT.marginal_posterior.din IRT.marginal_posterior.gdina IRT.marginal_posterior.mcdina |
| S3 Methods for Assessing Model Fit | IRT.modelfit IRT.modelfit.din IRT.modelfit.gdina summary.IRT.modelfit.din summary.IRT.modelfit.gdina |
| S3 Method for Extracting a Parameter Table | IRT.parameterTable |
| Generation of a Replicate Design for 'IRT.jackknife' | IRT.repDesign |
| Root Mean Square Deviation (RMSD) Item Fit Statistic | IRT.RMSD IRT_RMSD_calc_rmsd summary.IRT.RMSD |
| Create Dataset with Group-Specific Items | item_by_group |
| RMSEA Item Fit | itemfit.rmsea |
| S-X2 Item Fit Statistic for Dichotomous Data | itemfit.sx2 plot.itemfit.sx2 summary.itemfit.sx2 |
| Extract Log-Likelihood | logLik.din logLik.gdina logLik.gdm logLik.mcdina logLik.reglca logLik.slca |
| Multiple Choice DINA Model | mcdina print.mcdina summary.mcdina |
| Assessing Model Fit and Local Dependence by Comparing Observed and Expected Item Pair Correlations | modelfit.cor modelfit.cor.din modelfit.cor2 summary.modelfit.cor.din |
| Numerical Computation of the Hessian Matrix | numerical_gradient numerical_Hessian numerical_Hessian_partial |
| Opens and Closes a 'sink' Connection | csink osink |
| Appropriateness Statistic for Person Fit Assessment | personfit.appropriateness plot.personfit.appropriateness summary.personfit.appropriateness |
| S3 Methods for Plotting Item Probabilities | plot_item_mastery plot_item_mastery.din plot_item_mastery.gdina |
| Plot Method for Objects of Class din | plot.din |
| Expected Values and Predicted Probabilities from Item Response Response Models | IRT.predict predict.din predict.gdina predict.gdm predict.mcdina predict.slca |
| Print Method for Objects of Class summary.din | print.summary.din |
| Regularized Latent Class Analysis | reglca summary.reglca |
| Constructing a Dataset with Sequential Pseudo Items for Ordered Item Responses | sequential.items |
| Simulate an Item Response Model | sim_model |
| Data Simulation Tool for DINA, DINO and mixed DINA and DINO Data | sim.din |
| Simulation of the GDINA model | sim.gdina sim.gdina.prepare |
| Tetrachoric or Polychoric Correlations between Attributes | skill.cor skill.polychor |
| Skill Space Approximation | skillspace.approximation |
| Creation of a Hierarchical Skill Space | skillspace.full skillspace.hierarchy |
| Structured Latent Class Analysis (SLCA) | plot.slca print.slca slca summary.slca |
| Prints 'summary' and 'sink' Output in a File | summary_sink |
| Summary Method for Objects of Class din | summary.din |
| Asymptotic Covariance Matrix, Standard Errors and Confidence Intervals | confint.din IRT.se IRT.se.din vcov.din |
| Wald Test for a Linear Hypothesis | WaldTest |
