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ForceChoice 1.0.0 (2026-07-12)

New Features

Eight Model Families

• fit.MIRT() - Multidimensional Item Response Theory (1PL–4PL) for binary dominance data, with optional Q-matrix structure.
• fit.MGPCM() - Multidimensional Generalized Partial Credit Model for polytomous ordered-category responses.
• fit.MGGUM() - Multidimensional Generalized Graded Unfolding Model for ideal-point polytomous responses with signed Q-matrix.
• fit.FCMIRT() - Forced-Choice MIRT with item-level dominance endorsement and Luce–Plackett block-level ranking (RANK, MOLE, PICK).
• fit.FCGGUM() - Forced-Choice GGUM with item-level ideal-point endorsement and block-level ranking.
• fit.TIRT() - Thurstonian IRT for forced-choice with pairwise probit comparisons and latent utility differences.
• fit.FCDCM() - Forced-Choice Diagnostic Classification Model with higher-order latent trait, DINA/DINO condensation rules, and exact attribute-profile marginalization.
• fit.FCGDINA() - Forced-Choice GDINA model with DINA, DINO, ACDM, and GDINA item-level structures for ranking, most-least, and pick responses.

Estimation Backends

• Stan (method = "stan"): Full Bayesian inference via Hamiltonian Monte Carlo (NUTS/HMC) with rstan.
• iStEM (method = "iStEM"): Fast iterative Stochastic EM with Metropolis-within-Gibbs person sampling and L-BFGS-B item optimization.
• EM (method = "EM"): Deterministic posterior-weight EM for FCGDINA.

Comprehensive Goodness-of-Fit

• Limited-information M2 statistic (Maydeu-Olivares & Joe, 2005, 2006)
• RMSEA with confidence intervals, SRMSR, RMSR
• Comparative fit: CFI, TLI, IFI
• Information criteria: AIC, AICc, BIC, CAIC, SABIC, HQIC
• Pseudo-R2 measures: McFadden, Cox–Snell, Nagelkerke, and more
• Local dependence: Yen’s Q3 (raw and adjusted)
• Posterior diagnostics: entropy, classification accuracy

Data Simulation

• sim.data.MIRT(), sim.data.MGPCM(), sim.data.MGGUM() for traditional item response data.
• sim.data.FCMIRT(), sim.data.FCGGUM(), sim.data.TIRT(), sim.data.FCDCM(), sim.data.FCGDINA() for forced-choice data.
• All simulators support controlled parameter generation, factor correlation, and rotation.

Solution Rotation

• rotate.MIRT() and rotate.matrix() for post-hoc rotation of MIRT solutions using promax or any GPArotation method.

S3 Methods

• Consistent generics across all model classes: coef(), confint(), deviance(), fitted(), logLik(), nobs(), plot(), predict(), print(), residuals(), summary(), update(), vcov().

Bug Fixes

• This is the initial CRAN release.

Notes

• The package was previously developed under the name fcirt and has been substantially rewritten and expanded to support eight model families and the iStEM estimation backend.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.