Package: MultiLCIRT 2.11

Francesco Bartolucci

MultiLCIRT: Multidimensional Latent Class Item Response Theory Models

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

Authors:Francesco Bartolucci, Silvia Bacci, Michela Gnaldi - University of Perugia

MultiLCIRT_2.11.tar.gz
MultiLCIRT_2.11.tar.gz(r-4.5-noble)MultiLCIRT_2.11.tar.gz(r-4.4-noble)
MultiLCIRT_2.11.tgz(r-4.4-emscripten)MultiLCIRT_2.11.tgz(r-4.3-emscripten)
MultiLCIRT.pdf |MultiLCIRT.html✨
MultiLCIRT/json (API)

# Install 'MultiLCIRT' in R:
install.packages('MultiLCIRT', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • hads - Dataset about measurement of anxiety and depression in oncological patients
  • naep - NAEP dataset

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortran

1.86 score 1 stars 2 packages 12 scripts 454 downloads 22 exports 4 dependencies

Last updated 8 years agofrom:2463a23604. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 05 2024
R-4.5-linux-x86_64OKDec 05 2024

Exports:aggr_dataclass_itemcompare_modelsest_multi_globest_multi_polyest_multi_poly_clustinv_globlk_obs_scorelk_obs_score_clustmatr_globprint.class_itemprint.est_multi_polyprint.est_multi_poly_clustprint.test_dimprob_multi_globsearch.modelstandard.matrixsummary.class_itemsummary.est_multi_polysummary.est_multi_poly_clustsummary.test_dimtest_dim

Dependencies:limSolvelpSolveMASSquadprog

Readme and manuals

Help Manual

Help pageTopics
Multidimensional Latent Class (LC) Item Response Theory (IRT) ModelsMultiLCIRT-package MultiLCIRT
Aggregate dataaggr_data
Hierarchical classification of test itemsclass_item
Compare different models fitted by est_multi_polycompare_models
Fit marginal regression models for categorical responsesest_multi_glob
Estimate multidimensional LC IRT model for dichotomous and polytomous responsesest_multi_poly
Estimate multidimensional and multilevel LC IRT model for dichotomous and polytomous responsesest_multi_poly_clust
Dataset about measurement of anxiety and depression in oncological patientshads
Invert marginal logitsinv_glob
Compute observed log-likelihood and scorelk_obs_score
Compute observed log-likelihood and scorelk_obs_score_clust
Matrices to compute generalized logitsmatr_glob
NAEP datasetnaep
Print the output of class_item objectprint.class_item
Print the output of est_multi_poly objectprint.est_multi_poly
Print the output of est_multi_poly_clust objectprint.est_multi_poly_clust
Print the output of test_dim objectprint.test_dim
Global probabilitiesprob_multi_glob
Search for the global maximum of the log-likelihoodsearch.model
Standardization of a matrix of support points on the basis of a vector of probabilitiesstandard.matrix
Print the output of class_item objectsummary.class_item
Print the output of test_dim objectsummary.est_multi_poly
Print the output of est_multi_poly_clust objectsummary.est_multi_poly_clust
Print the output of test_dim objectsummary.test_dim
Likelihood ratio testing between nested multidimensional LC IRT modelstest_dim