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
        (CDM). The package includes functions for estimating CDMs such
        as the restricted DINA or DINO (R-DINA or R-DINO) models
        (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the
        G-DINA model for forced-choice blocks (Nájera et al., 2024;
        <doi:10.1111/bmsp.12393>), and the general nonparametric
        classification method (Chiu et al., 2018;
        <doi:10.1007/s11336-017-9595-4>). Additionally, methods for
        identifying the latent structure of CDMs are also available,
        such as dimensionality assessment via parallel analysis and
        automated fit comparison (Nájera et al., 2021;
        <doi:10.3389/fpsyg.2021.614470>), as well as empirical Q-matrix
        validation and estimation using the Hull method (Nájera et al.,
        2021; <doi:10.1111/bmsp.12228>) and the discrete factor loading
        method (Wang et al., 2018; <doi:10.1007/978-3-319-77249-3_29>).
        Other practical functions for CDM applications include
        corrected classification accuracy estimation via multiple
        imputation (Kreitchmann et al., 2022;
        <doi:10.3758/s13428-022-01967-5>), model-based recursive
        partitioning to detect non-invariant subpopulations (Nájera et
        al., in press), person-fit evaluation (Santos et al., 2020;
        <doi:10.1007/s00357-019-09325-5>), and model identifiability
        assessment (Gu and Xu, 2021; <doi:10.5705/ss.202018.0410>).
        Lastly, the package also provides useful functions for CDM
        simulation studies, such as random Q-matrix generation and
        forced-choice data generation.
License: GPL-3
Depends: R (>= 3.6.0)
Imports: GDINA (>= 2.8.0), ggplot2 (>= 3.3.0), psych (>= 1.9.12), sirt
        (>= 3.9-4), parallel (>= 3.6.3), stats (>= 3.6.3), GPArotation
        (>= 2014.11-1), combinat (>= 0.0-8), fungible, foreach, doSNOW,
        plyr, partykit
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],
        Francisco J. Abad [aut, cph], Rodrigo S. Kreitchmann [ctb],
        Kevin 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
        zlib1g-dev libclang-dev
Repository: https://pablo-najera.r-universe.dev
Date/Publication: 2026-05-05 11:06:31 UTC
RemoteUrl: https://github.com/pablo-najera/cdmtools
RemoteRef: HEAD
RemoteSha: a0edc5209a1bf8f4dae52dafd17a1d1916d78d74
NeedsCompilation: no
Packaged: 2026-09-22 11:58:18 UTC; root