Package: tirt
Title: Testlet Item Response Theory
Version: 0.4.0
Authors@R: c(
    person("Jiawei", "Xiong", email = "jiawei.xiong@uga.edu", role = c("aut", "cre")),
    person("Cheng", "Tang", role = "ctb"),
    person("Qidi", "Liu", role = "ctb")
  )
Description: Implementation of Testlet and Item Response Theory. A
        light-version yet comprehensive and streamlined framework for
        psychometric analysis using unidimensional and multidimensional
        Item Response Theory (IRT; Baker & Kim (2004)
        <doi:10.1201/9781482276725>) and Testlet Response Theory (TRT;
        Wainer et al., (2007) <doi:10.1017/CBO9780511618765>). Designed
        for researchers, this package supports the estimation of item
        and person parameters for a wide variety of models, including
        binary (i.e., Rasch, 2-Parameter Logistic, 3-Parameter
        Logistic) and polytomous (Partial Credit Model, Generalized
        Partial Credit Model, Graded Response Model) formats. It also
        supports the estimation of Testlet models (Rasch Testlet,
        2-Parameter Logistic Testlet, 3-Parameter Logistic Testlet,
        Bifactor, Partial Credit Model Testlet, Graded Response),
        allowing users to account for local item dependence in bundled
        items. A key feature is the specialized support for combination
        use and joint estimation of item response model and testlet
        response model in one calibration. Beyond standard estimation
        via Marginal Maximum Likelihood with Expectation-Maximization
        (EM) or Joint Maximum Likelihood, the package also offers
        Bayesian estimation using priors with maximum a posteriori
        (MAP) method for unidimensional item response theory models. It
        also provides functions for scale linking and equating
        (Mean-Mean, Mean-Sigma, Stocking-Lord) to ensure comparability
        across mixed-format test forms. It also facilitates
        fixed-parameter calibration, enabling users to estimate person
        abilities with known item parameters or vice versa, which is
        essential for pre-equating studies and item bank maintenance.
        Comprehensive data simulation functions are included to
        generate synthetic datasets with complex structures, including
        mixed-model blocks and specific testlet effects, aiding in
        methodological research and study design validation.
        Researchers can try multiple simulation situations. A suite of
        post-estimation tools is also provided, including item and test
        information functions with the conditional standard error of
        measurement, summed-score to scale-score conversion tables
        (expected a posteriori, weighted likelihood, and maximum
        likelihood), person-fit and item-fit statistics, local
        dependence diagnostics (Yen's Q3), differential item
        functioning (M-H and logistic regression), reliability
        coefficients, test characteristic curves, and mixture
        (latent-class) item response models.
License: GPL-3
Encoding: UTF-8
LazyData: true
LazyDataCompression: xz
RoxygenNote: 7.3.3
Depends: R (>= 4.1.0)
Imports: dplyr, tidyr, purrr, gtools, stats, utils
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2026-09-19 08:59:43 UTC; root
Author: Jiawei Xiong [aut, cre], Cheng Tang [ctb], Qidi Liu [ctb]
Maintainer: Jiawei Xiong <jiawei.xiong@uga.edu>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-08-20 08:10:02 UTC
RemoteUrl: https://github.com/cran/tirt
RemoteRef: HEAD
RemoteSha: 68c7a8878921919afe9f814f0a4038c38b3eb395
