{ "_id": "6a4794ce2f27a93436c0dd2f", "Package": "exametrika", "Type": "Package", "Title": "Test Theory Analysis and Biclustering", "Version": "1.15.0", "Authors@R": "person(\"Koji Kosugi\", , , \"kosugitti@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-5816-0099\"))", "Description": "Implements comprehensive test data engineering methods as\ndescribed in Shojima (2022, ISBN:978-9811699856). Provides\nstatistical techniques for engineering and processing test\ndata: Classical Test Theory (CTT) with reliability coefficients\nfor continuous ability assessment; Item Response Theory (IRT)\nincluding Rasch, 2PL, and 3PL models with item/test information\nfunctions; Latent Class Analysis (LCA) for nominal clustering;\nLatent Rank Analysis (LRA) for ordinal clustering with\nautomatic determination of cluster numbers; Biclustering\nmethods including infinite relational models for simultaneous\nclustering of examinees and items without predefined cluster\nnumbers; and Bayesian Network Models (BNM) for visualizing\ninter-item dependencies. Features local dependence analysis\nthrough LRA and biclustering, parameter estimation,\ndimensionality assessment, and network structure visualization\nfor educational, psychological, and social science research.", "License": "MIT + file LICENSE", "Language": "en-US", "Encoding": "UTF-8", "LazyData": "true", "BuildVignettes": "true", "URL": "https://kosugitti.github.io/exametrika/,\nhttps://github.com/kosugitti/exametrika", "BugReports": "https://github.com/kosugitti/exametrika/issues", "Roxygen": "list(markdown = TRUE)", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/pak/sysreqs": "libglpk-dev libxml2-dev", "Repository": "https://kosugitti.r-universe.dev", "Date/Publication": "2026-07-03 01:04:12 UTC", "RemoteUrl": "https://github.com/kosugitti/exametrika", "RemoteRef": "HEAD", "RemoteSha": "f8bc4ea57f47172a88c356eb7cc5b92a023ad93d", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-07-03 03:03:26 UTC", "User": "root" }, "Author": 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"CCRR", "CCRR.binary", "CCRR.default", "CCRR.nominal" ] }, { "page": "chatterjee_matrix", "title": "Pairwise Chatterjee's xi correlation matrix", "topics": [ "chatterjee_matrix" ] }, { "page": "chatterjee_xi", "title": "Chatterjee's xi correlation coefficient", "topics": [ "chatterjee_xi" ] }, { "page": "crr", "title": "Correct Response Rate", "topics": [ "crr", "crr.binary", "crr.default" ] }, { "page": "CSR", "title": "Conditional Selection Rate", "topics": [ "CSR" ] }, { "page": "CTT", "title": "Classical Test Theory", "topics": [ "CTT" ] }, { "page": "dataFormat", "title": "dataFormat", "topics": [ "dataFormat" ] }, { "page": "Dimensionality", "title": "Dimensionality", "topics": [ "Dimensionality", "Dimensionality.binary", "Dimensionality.default", "Dimensionality.ordinal", "Dimensionality.rated" ] }, { "page": "DistractorAnalysis", "title": "Distractor Analysis", "topics": [ "DistractorAnalysis", "DistractorAnalysis.LRArated", "DistractorAnalysis.ratedBiclustering", "plot.DistractorAnalysis", "print.DistractorAnalysis" ] }, { "page": "Glasso", "title": "Graphical Lasso for Gaussian Graphical Models", "topics": [ "Glasso" ] }, { "page": "GridSearch", "title": "Grid Search for Optimal Parameters", "topics": [ "GridSearch" ] }, { "page": "GRM", "title": "Graded Response Model (GRM)", "topics": [ "GRM" ] }, { "page": "grm_iif", "title": "Item Information Function for GRM", "topics": [ "grm_iif" ] }, { "page": "grm_prob", "title": "Probability function for GRM", "topics": [ "grm_prob" ] }, { "page": "IIF2PLM", "title": "IIF for 2PLM", "topics": [ "IIF2PLM" ] }, { "page": "IIF3PLM", "title": "IIF for 3PLM", "topics": [ "IIF3PLM" ] }, { "page": "InterItemAnalysis", "title": "Inter-Item Analysis for Psychometric Data", "topics": [ "InterItemAnalysis" ] }, { "page": "IRM", "title": "IRM (Deprecated)", "topics": [ "IRM" ] }, { "page": "IRT", "title": "Estimating Item parameters using EM algorithm", "topics": [ "IRT" ] }, { "page": "ITBiserial", "title": 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Threshold", "topics": [ "ItemThreshold", "ItemThreshold.binary", "ItemThreshold.ordinal" ] }, { "page": "ItemTotalCorr", "title": "Item-Total Correlation", "topics": [ "ItemTotalCorr", "ItemTotalCorr.binary", "ItemTotalCorr.default", "ItemTotalCorr.ordinal" ] }, { "page": "J12S5000", "title": "J12S5000", "topics": [ "J12S5000" ] }, { "page": "J15S3810", "title": "J15S3810", "topics": [ "J15S3810" ] }, { "page": "J15S500", "title": "J15S500", "topics": [ "J15S500" ] }, { "page": "J20S400", "title": "J20S400", "topics": [ "J20S400" ] }, { "page": "J20S600", "title": "J20S600", "topics": [ "J20S600" ] }, { "page": "J21S300", "title": "J21S300", "topics": [ "J21S300" ] }, { "page": "J35S500", "title": "J35S500", "topics": [ "J35S500" ] }, { "page": "J35S5000", "title": "J35S5000", "topics": [ "J35S5000" ] }, { "page": "J35S515", "title": "J35S515", "topics": [ "J35S515" ] }, { "page": "J50S100", "title": "J50S100", "topics": [ "J50S100" ] }, { "page": "J5S10", "title": "J5S10", "topics": [ "J5S10" ] }, { "page": "J5S1000", "title": "J5S1000", "topics": [ "J5S1000" ] }, { "page": "JCRR", "title": "Joint Correct Response Rate", "topics": [ "JCRR", "JCRR.binary", "JCRR.default", "JCRR.nominal" ] }, { "page": "JointSampleSize", "title": "Joint Sample Size", "topics": [ "JointSampleSize", "JointSampleSize.binary", "JointSampleSize.default" ] }, { "page": "JSR", "title": "Joint Selection Rate", "topics": [ "JSR" ] }, { "page": "LCA", "title": "Latent Class Analysis", "topics": [ "LCA" ] }, { "page": "LD_param_est", "title": "LDparam set", "topics": [ "LD_param_est" ] }, { "page": "LDB", "title": "Local Dependence Biclustering", "topics": [ "LDB" ] }, { "page": "LDLRA", "title": "Local Dependence Latent Rank Analysis", "topics": [ "LDLRA" ] }, { "page": "LDLRA_PBIL", "title": "Structure Learning for LDLRA by PBIL algorithm", "topics": [ "LDLRA_PBIL" ] }, { "page": "LogisticModel", "title": "Four-Parameter Logistic Model", "topics": [ "LogisticModel" ] }, { "page": "longdataFormat", "title": "Long Format Data Conversion", "topics": [ "longdataFormat" ] }, { "page": "LRA", "title": "Latent Rank Analysis", "topics": [ "LRA", "LRA.binary", "LRA.default", "LRA.ordinal", "LRA.rated" ] }, { "page": "maxParents_penalty", "title": "Utility function for searching DAG", "topics": [ "maxParents_penalty" ] }, { "page": "MutualInformation", "title": "Mutual Information", "topics": [ "MutualInformation", "MutualInformation.binary", "MutualInformation.default", "MutualInformation.ordinal" ] }, { "page": "nrs", "title": "Number Right Score", "topics": [ "nrs", "nrs.binary", "nrs.default" ] }, { "page": "objective_function_IRT", "title": "Log-likelihood function used in the Maximization Step (M-Step).", "topics": [ "objective_function_IRT" ] }, { "page": "OmegaCoefficient", "title": "Omega Coefficient", "topics": [ "OmegaCoefficient" ] }, { "page": "passage", "title": "Passage Rate of Student", "topics": [ "passage", "passage.binary", "passage.default" ] }, { 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