{
  "_id": "6a212273cd65a98ecbd1f8ba",
  "Package": "cdmTools",
  "Type": "Package",
  "Title": "Useful Tools for Cognitive Diagnosis Modeling",
  "Version": "1.0.7",
  "Date": "2026-05-04",
  "Authors@R": "c(person(given = \"Pablo\", family = \"Nájera\", role = c(\"aut\", \"cre\", \"cph\"), email = \"p.najeraalvarez@gmail.com\"), person(given = \"Miguel A.\", family = \"Sorrel\", role = c(\"aut\", \"cph\"), email = \"miguel.sorrel@uam.es\"), person(given = \"Francisco J.\", family = \"Abad\", role = c(\"aut\", \"cph\")), person(given = \"Rodrigo S.\", family = \"Kreitchmann\", role = c(\"ctb\")), person(given = \"Kevin\", family = \"Santos\", role = c(\"ctb\")), person(given = \"David\", family = \"Goretzko\", role = c(\"ctb\")), person(given = \"Philipp\", family = \"Sterner\", role = c(\"ctb\")))",
  "Description": "Provides useful tools for cognitive diagnosis modeling\n(CDM). The package includes functions for estimating CDMs such\nas the restricted DINA or DINO (R-DINA or R-DINO) models\n(Nájera et al., 2023; <doi:10.3102/10769986231158829>), the\nG-DINA model for forced-choice blocks (Nájera et al., 2024;\n<doi:10.1111/bmsp.12393>), and the general nonparametric\nclassification method (Chiu et al., 2018;\n<doi:10.1007/s11336-017-9595-4>). Additionally, methods for\nidentifying the latent structure of CDMs are also available,\nsuch as dimensionality assessment via parallel analysis and\nautomated fit comparison (Nájera et al., 2021;\n<doi:10.3389/fpsyg.2021.614470>), as well as empirical Q-matrix\nvalidation and estimation using the Hull method (Nájera et al.,\n2021; <doi:10.1111/bmsp.12228>) and the discrete factor loading\nmethod (Wang et al., 2018; <doi:10.1007/978-3-319-77249-3_29>).\nOther practical functions for CDM applications include\ncorrected classification accuracy estimation via multiple\nimputation (Kreitchmann et al., 2022;\n<doi:10.3758/s13428-022-01967-5>), model-based recursive\npartitioning to detect non-invariant subpopulations (Nájera et\nal., in press), person-fit evaluation (Santos et al., 2020;\n<doi:10.1007/s00357-019-09325-5>), and model identifiability\nassessment (Gu and Xu, 2021; <doi:10.5705/ss.202018.0410>).\nLastly, the package also provides useful functions for CDM\nsimulation studies, such as random Q-matrix generation and\nforced-choice data generation.",
  "License": "GPL-3",
  "URL": "https://github.com/pablo-najera/cdmTools",
  "BugReports": "https://github.com/pablo-najera/cdmTools/issues",
  "Encoding": "UTF-8",
  "Author": "Pablo Nájera [aut, cre, cph], Miguel A. Sorrel [aut, cph],\nFrancisco J. Abad [aut, cph], Rodrigo S. Kreitchmann [ctb],\nKevin Santos [ctb], David Goretzko [ctb], Philipp Sterner [ctb]",
  "Maintainer": "Pablo Nájera <p.najeraalvarez@gmail.com>",
  "Config/roxygen2/version": "8.0.0",
  "Config/pak/sysreqs": "cmake libgmp3-dev make libuv1-dev pkg-config\nzlib1g-dev libclang-dev",
  "Repository": "https://pablo-najera.r-universe.dev",
  "Date/Publication": "2026-05-05 11:06:31 UTC",
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  "RemoteRef": "HEAD",
  "RemoteSha": "a0edc5209a1bf8f4dae52dafd17a1d1916d78d74",
  "NeedsCompilation": "no",
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    "User": "root"
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