Overview | Model Families | Model and Backend Coverage | Estimation Backends | Installation | Quick Start: Traditional IRT | Simulating and Fitting a MIRT Model | Goodness-of-Fit Evaluation | Forced-Choice Modeling | Simulating and Fitting FCMIRT | Thurstonian IRT (TIRT) | Forced-Choice DCM (FCDCM) | Forced-Choice GDINA (FCGDINA) | Working with Polytomous Data | MGPCM (Dominance) | MGGUM (Ideal-Point / Unfolding) | Solution Rotation | Diagnostic Plots | Estimation Control | Common Parameters (top-level) | Stan-Specific Control | iStEM-Specific Control (advanced) | References
Package: ForceChoice 1.0.1
ForceChoice: Forced-Choice Modeling Based on Item Response Theory and Cognitive Diagnostic Models
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).
Authors:
ForceChoice_1.0.1.tar.gz
ForceChoice_1.0.1.zip(r-4.7-x86_64)ForceChoice_1.0.1.zip(r-4.7-arm64)ForceChoice_1.0.1.zip(r-4.6-x86_64)ForceChoice_1.0.1.zip(r-4.6-arm64)ForceChoice_1.0.1.zip(r-4.5-x86_64)
ForceChoice_1.0.1.tgz(r-4.6-x86_64)ForceChoice_1.0.1.tgz(r-4.6-arm64)ForceChoice_1.0.1.tgz(r-4.5-x86_64)ForceChoice_1.0.1.tgz(r-4.5-arm64)
ForceChoice_1.0.1.tar.gz(r-4.7-arm64)ForceChoice_1.0.1.tar.gz(r-4.7-x86_64)ForceChoice_1.0.1.tar.gz(r-4.6-arm64)ForceChoice_1.0.1.tar.gz(r-4.6-x86_64)
ForceChoice_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ForceChoice/json (API)
| # Install 'ForceChoice' in R: |
| install.packages('ForceChoice', repos = c('https://haijiangq.r-universe.dev', 'https://cloud.r-project.org')) |
Datasets:
- triplets - Triplets of Pairwise Comparisons
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:7b635b5f5c. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 409 | ||
| linux-devel-x86_64 | OK | 731 | ||
| source / vignettes | OK | 1114 | ||
| linux-release-arm64 | OK | 588 | ||
| linux-release-x86_64 | OK | 511 | ||
| macos-release-arm64 | OK | 620 | ||
| macos-release-x86_64 | OK | 1193 | ||
| macos-oldrel-arm64 | OK | 577 | ||
| macos-oldrel-x86_64 | OK | 985 | ||
| windows-devel-arm64 | OK | 1060 | ||
| windows-devel-x86_64 | OK | 785 | ||
| windows-release-arm64 | OK | 844 | ||
| windows-release-x86_64 | OK | 881 | ||
| windows-oldrel-x86_64 | OK | 838 | ||
| wasm-release | OK | 459 |
Exports:extractfit.FCDCMfit.FCGDINAfit.FCGGUMfit.FCMIRTfit.MGGUMfit.MGPCMfit.MIRTfit.TIRTget.block.items.from.dataget.block.items.from.data.FCDCMget.data.from.responseget.data.from.response.FCDCMget.data.from.response.TIRTget.fit.indexget.log_likget.response.from.dataget.response.from.data.FCDCMget.response.from.data.TIRTgood.of.fitmodel.FCGDINArotatesim.data.FCDCMsim.data.FCGDINAsim.data.FCGGUMsim.data.FCMIRTsim.data.MGGUMsim.data.MGPCMsim.data.MIRTsim.data.TIRTsummary.good.of.fit
Dependencies:abindbackportsBHcallrcheckmateclicodacpp11descdistributionalfarvergenericsggplot2glueGPArotationgridExtragtableinlineisobandlabelinglatticelifecycleloomagrittrMASSmatrixStatsnumDerivotelpillarpkgbuildpkgconfigposteriorprocessxpsQuickJSRR6RColorBrewerRcppRcppArmadilloRcppEigenRcppParallelrlangrstanrstantoolsS7scalesStanHeaderstensorAtibbleutf8vctrsviridisLitewithr
Introduction to ForceChoice
Last update: 2026-07-30
Started: 2026-07-30
ForceChoice Model Theory and Selection Guide
Overview | Traditional (Single-Stimulus) Models | MIRT: Multidimensional Item Response Theory | MGPCM: Multidimensional Generalized Partial Credit Model | MGGUM: Multidimensional Generalized Graded Unfolding Model | Forced-Choice (Comparative) Models | FCMIRT: Forced-Choice MIRT | FCGGUM: Forced-Choice GGUM | TIRT: Thurstonian IRT for Forced-Choice | FCDCM: Forced-Choice Diagnostic Classification Model | FCGDINA: Forced-Choice GDINA | Model Selection Guide | Estimation Method Selection | Goodness-of-Fit Framework | References
Last update: 2026-07-30
Started: 2026-07-30
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| ForceChoice: Forced-Choice Modeling Based on IRT and CDM | ForceChoice-package ForceChoice |
| S3 Methods: coef | coef coef.FCDCM coef.FCGDINA coef.FCGGUM coef.FCMIRT coef.MGGUM coef.MGPCM coef.MIRT coef.TIRT |
| S3 Methods: confint | confint confint.FCDCM confint.FCGDINA confint.FCGGUM confint.FCMIRT confint.MGGUM confint.MGPCM confint.MIRT confint.TIRT |
| S3 Methods: deviance | deviance deviance.FCDCM deviance.FCGDINA deviance.FCGGUM deviance.FCMIRT deviance.MGGUM deviance.MGPCM deviance.MIRT deviance.TIRT |
| S3 Methods: extract | extract extract.FCDCM extract.FCGDINA extract.FCGGUM extract.FCMIRT extract.MGGUM extract.MGPCM extract.MIRT extract.TIRT |
| Fit the Forced-Choice Diagnostic Classification Model (FCDCM) | fit.FCDCM |
| Fit the Forced-Choice GDINA Model | fit.FCGDINA |
| Fit the Forced-Choice Generalized Graded Unfolding Model (FCGGUM) | fit.FCGGUM |
| Fit the Forced-Choice Multidimensional IRT (FCMIRT) Model | fit.FCMIRT |
| Fit the Multidimensional Generalized Graded Unfolding Model (MGGUM) | fit.MGGUM |
| Fit the Multidimensional Generalized Partial Credit Model (MGPCM) | fit.MGPCM |
| Fit the Multidimensional Item Response Theory (MIRT) Model | fit.MIRT |
| Fit the Thurstonian Item Response Theory (TIRT) Model | fit.TIRT |
| S3 Methods: fitted | fitted fitted.FCDCM fitted.FCGDINA fitted.FCGGUM fitted.FCMIRT fitted.MGGUM fitted.MGPCM fitted.MIRT fitted.TIRT |
| Forced-Choice Data Conversion Formats | forced_choice_data_conversion |
| Extract Block Items from Forced-Choice Data | get.block.items.from.data |
| Extract Block Items from FCDCM Data | get.block.items.from.data.FCDCM |
| Convert an Integer Response (Pattern-Index) Matrix to FC Data Strings | get.data.from.response |
| Convert an FCDCM Response Matrix to FC Data Strings | get.data.from.response.FCDCM |
| Convert TIRT Pairwise-Response Matrix to Forced-Choice Data Strings | get.data.from.response.TIRT |
| Goodness-of-Fit Indices for Fitted Models | get.fit.index get.fit.index.FCDCM get.fit.index.FCGDINA get.fit.index.FCGGUM get.fit.index.FCMIRT get.fit.index.MGGUM get.fit.index.MGPCM get.fit.index.MIRT get.fit.index.TIRT |
| Extract pointwise log-likelihood matrix | get.log_lik get.log_lik.default |
| Convert FC Data Strings to an Integer Response (Pattern-Index) Matrix | get.response.from.data |
| Convert FCDCM Data Strings to a Binary Response Matrix | get.response.from.data.FCDCM |
| Convert TIRT Forced-Choice Data to Pairwise-Response Matrix | get.response.from.data.TIRT |
| Goodness-of-Fit Indices for Latent Probability Models | good.of.fit |
| S3 Methods: nobs | nobs nobs.FCDCM nobs.FCGDINA nobs.FCGGUM nobs.FCMIRT nobs.MGGUM nobs.MGPCM nobs.MIRT nobs.TIRT |
| S3 Methods: plot | plot plot.FCDCM plot.FCGDINA plot.FCGGUM plot.FCMIRT plot.MGGUM plot.MGPCM plot.MIRT plot.TIRT |
| S3 Methods: predict | predict predict.FCDCM predict.FCGDINA predict.FCGGUM predict.FCMIRT predict.MGGUM predict.MGPCM predict.MIRT predict.TIRT |
| S3 Methods: print | print print.data.FCDCM print.data.FCGDINA print.data.FCGGUM print.data.FCMIRT print.data.MGGUM print.data.MGPCM print.data.MIRT print.data.TIRT print.FCDCM print.FCGDINA print.FCGGUM print.FCMIRT print.good.of.fit print.MGGUM print.MGPCM print.MIRT print.summary.FCDCM print.summary.FCGDINA print.summary.FCGGUM print.summary.FCMIRT print.summary.good.of.fit print.summary.MGGUM print.summary.MGPCM print.summary.MIRT print.summary.TIRT print.TIRT |
| S3 Methods: residuals | residuals residuals.FCDCM residuals.FCGDINA residuals.FCGGUM residuals.FCMIRT residuals.MGGUM residuals.MGPCM residuals.MIRT residuals.TIRT |
| S3 Methods: rotate | rotate rotate.MGPCM rotate.MIRT |
| Simulate Data from the Forced-Choice DCM Model | sim.data.FCDCM |
| Simulate Forced-Choice GDINA Data | sim.data.FCGDINA |
| Simulate Data from the Forced-Choice GGUM Model | sim.data.FCGGUM |
| Simulate FCMIRT Data | sim.data.FCMIRT |
| Simulate Data from the Multidimensional Generalized Graded Unfolding Model | sim.data.MGGUM |
| Simulate Data from the Multidimensional Generalized Partial Credit Model | sim.data.MGPCM |
| Simulate Data from the Multidimensional IRT (MIRT) Model | sim.data.MIRT |
| Simulate Data from the Thurstonian IRT (TIRT) Model | sim.data.TIRT |
| S3 Methods: summary | summary summary.FCDCM summary.FCGDINA summary.FCGGUM summary.FCMIRT summary.good.of.fit summary.MGGUM summary.MGPCM summary.MIRT summary.TIRT |
| Triplets of Pairwise Comparisons | triplets |
| S3 Methods: update | update update.FCDCM update.FCGDINA update.FCGGUM update.FCMIRT update.MGGUM update.MGPCM update.MIRT update.TIRT |
| S3 Methods: vcov | vcov vcov.FCDCM vcov.FCGDINA vcov.FCGGUM vcov.FCMIRT vcov.MGGUM vcov.MGPCM vcov.MIRT vcov.TIRT |