Item Response Theory Calibration with a Mixed Subjects Design


[Up] [Top]

Documentation for package ‘mixedsubjectsirt’ version 1.0.0

Help Pages

ability_gradient Gradient of ML ability scores with respect to item parameters
ability_gradient_1pl Gradient of ML ability scores w.r.t. 1PL item parameters
ability_risk Propagated ability risk from item-parameter uncertainty
ability_risk_1pl Propagated ability risk for a 1PL fit
diagnose_lambda_grid Diagnose lambda values over a grid
fit_1pl Fit a 1PL (one-parameter logistic) model
fit_2pl Fit a unidimensional 2PL IRT model
fit_mixed_subjects Fit a mixed-subjects 2PL calibration
fit_mixed_subjects_1pl Fit a mixed-subjects 1PL calibration (frozen expected-count)
fit_mixed_subjects_from_quadrature Fit from precomputed quadrature summaries
fit_mixed_subjects_iterative Fit a mixed-subjects 2PL calibration with iterative EM
fit_mixed_subjects_mml Fit a mixed-subjects 2PL calibration via marginal maximum likelihood
fit_mixed_subjects_mml_1pl Fit a mixed-subjects 1PL calibration via marginal maximum likelihood
fit_mixed_subjects_split Fit a split-sample mixed-subjects 2PL calibration
link_item_parameters Link item parameters onto a target scale
make_quadrature Create a standard-normal Gauss-Hermite quadrature grid
mixed_subjects_loss Mixed-subjects objective function
mixed_subjects_quadrature Convert responses to quadrature form
posterior_weights_2pl Compute posterior quadrature weights for a 2PL model
score_theta Estimate ability scores from a 2PL calibration
simulate_2pl Simulate 2PL item responses
summarize_expected_counts Summarize response data as expected quadrature counts
tune_lambda_ability_risk Tune lambda by downstream ability-score risk
tune_lambda_ability_risk_1pl Tune lambda by downstream ability-score risk for a 1PL model
tune_lambda_ability_risk_crossfit Cross-fit ability-score-risk lambda tuning
tune_lambda_ability_risk_item Per-item ability-risk lambda tuning via coordinate descent
tune_lambda_ppi_score Plug-in PPI++ optimal tuning parameter
tune_lambda_ppi_score_1pl Plug-in PPI++ optimal tuning parameter for a 1PL model
tune_lambda_ppi_score_item Per-item PPI++ optimal tuning parameters
vcov_mixed_subjects Sandwich covariance for a mixed-subjects fit
vcov_mixed_subjects_1pl Sandwich covariance for a 1PL mixed-subjects fit
vcov_mixed_subjects_mml Marginal-MML sandwich covariance for a mixed-subjects fit