{ "_id": "6acf8c8ee2d863f662952617", "Package": "exametrika", "Type": "Package", "Title": "Test Data Engineering", "Version": "2.2.0", "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)", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/testthat/parallel": "true", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "libglpk-dev libxml2-dev", "Repository": "https://kosugitti.r-universe.dev", "Date/Publication": "2026-10-14 07:49:36 UTC", "RemoteUrl": "https://github.com/kosugitti/exametrika", "RemoteRef": "HEAD", "RemoteSha": "2d9dd8d7a3e62e330b1206175753b3f49b507919", "NeedsCompilation": "yes", "Packaged": { "Date": 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"Structure Learning for BNM by simple GA", "topics": [ "BNM_GA" ] }, { "page": "BNM_PBIL", "title": "Structure Learning for BNM by PBIL", "topics": [ "BNM_PBIL" ] }, { "page": "calcFitIndices", "title": "calc Fit Indices", "topics": [ "calcFitIndices" ] }, { "page": "CCRR", "title": "Conditional Correct Response Rate", "topics": [ "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": "IRT", "title": "Estimating Item parameters using EM algorithm", "topics": [ "IRT" ] }, { "page": "ITBiserial", "title": "Item-Total Biserial Correlation", "topics": [ "ITBiserial", "ITBiserial.binary", "ITBiserial.default" ] }, { "page": "ItemEntropy", "title": "Item Entropy", "topics": [ "ItemEntropy", "ItemEntropy.binary", "ItemEntropy.default", "ItemEntropy.ordinal" ] }, { "page": "ItemFit", "title": "Model Fit Functions for Items", "topics": [ "ItemFit" ] }, { "page": "ItemInformationFunc", "title": "IIF for 4PLM", "topics": [ "ItemInformationFunc" ] }, { "page": "ItemLift", "title": "Item Lift", "topics": [ "ItemLift", "ItemLift.binary", "ItemLift.default" ] }, { "page": "ItemOdds", "title": "Item Odds", "topics": [ "ItemOdds", "ItemOdds.binary", "ItemOdds.default" ] }, { "page": "ItemReport", "title": "Generate Item Report for Non-Binary Test Data", 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"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", "LCA.binary", "LCA.default", "LCA.nominal", "LCA.rated" ] }, { "page": "LD_param_est", "title": "LDparam set", "topics": [ "LD_param_est" ] }, { "page": "LDB", "title": "Local Dependence Biclustering", "topics": [ "LDB" ] }, { "page": "LDLRA", "title": 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