Package: ForceChoice
Title: Forced-Choice Modeling Based on Item Response Theory and
        Cognitive Diagnostic Models
Version: 1.0.1
Authors@R: c(person(given = "Haijiang", 
                    family = "Qin", 
                    role = c("aut", "cre", "cph"), 
                    email = "haijiang133@outlook.com", 
                    comment = c(ORCID = "0009-0000-6721-5653")),
             person(given = "Lei",
                    family = "Guo", 
                    role = c("aut", "cph"), 
                    email = "happygl1229@swu.edu.cn", 
                    comment = c(ORCID = "0000-0002-8273-3587")))
Description: Fits, simulates, and evaluates forced-choice and
        traditional item response theory (IRT) models for noncognitive
        assessment. Eight model families are supported, spanning
        dominance (multidimensional IRT (MIRT) 1PL--4PL;
        multidimensional generalized partial credit model (MGPCM)),
        ideal-point unfolding (multidimensional generalized graded
        unfolding model (MGGUM)), and forced-choice designs
        (forced-choice multidimensional IRT (FCMIRT), forced-choice
        generalized graded unfolding model (FCGGUM), Thurstonian IRT
        (TIRT), forced-choice diagnostic classification model (FCDCM),
        forced-choice generalized deterministic inputs, noisy "and"
        gate model (FCGDINA)) that mitigate response biases such as
        acquiescence and social desirability. Core estimation backends
        include full Bayesian inference via Hamiltonian Monte Carlo
        (Stan) and a fast improved stochastic expectation-maximization
        (iStEM) algorithm suitable for large-scale data; FCGDINA also
        provides a deterministic expectation-maximization (EM)
        estimator. Comprehensive model evaluation uses the
        limited-information M2 family of goodness-of-fit statistics
        (Maydeu-Olivares and Joe, 2005
        <doi:10.1198/016214504000002069>; 2006
        <doi:10.1007/s11336-005-1295-9>) together with root mean square
        error of approximation (RMSEA), comparative fit index (CFI),
        Tucker-Lewis index (TLI), and standardized root mean square
        residual (SRMSR).
License: GPL (>= 3)
Encoding: UTF-8
NeedsCompilation: yes
Biarch: true
Depends: R (>= 4.1.0)
Imports: coda, GPArotation, MASS, methods, numDeriv, parallel, Rcpp (>=
        0.12.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), rstantools
        (>= 2.7.1)
LinkingTo: BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppArmadillo, RcppEigen
        (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1),
        StanHeaders (>= 2.18.0)
SystemRequirements: GNU make
Suggests: knitr, loo, rmarkdown
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
Packaged: 2026-10-12 13:46:12 UTC; root
Author: Haijiang Qin [aut, cre, cph] (ORCID:
        <https://orcid.org/0009-0000-6721-5653>), Lei Guo [aut, cph]
        (ORCID: <https://orcid.org/0000-0002-8273-3587>)
Maintainer: Haijiang Qin <haijiang133@outlook.com>
Config/pak/sysreqs: cmake make
Repository: https://haijiangq.r-universe.dev
Date/Publication: 2026-09-12 17:10:02 UTC
RemoteUrl: https://github.com/cran/ForceChoice
RemoteRef: HEAD
RemoteSha: 7b635b5f5c74281991f9e819f76d844670e343aa