https://cjangelo.r-universe.devRecent package updates in cjangeloR-universehttps://github.com/cjangelo.pnghttps://cjangelo.r-universe.devSat, 12 Sep 2026 00:51:10 GMTcharles.iaconangelo@gmail.com (Charlie Iaconangelo)Estimate confirmatory latent class models for a variety of item response types that are encountered in the clinical field. One or two timepoints are supported. Latent regression estimation can be performed, allowing for comparisons of longitudinal latent class assignments (e.g., treatment success/failure) across observed groups (e.g., treatment arms in clinical trials). Fit statistics C2 (a limited-information goodness-of-fit statistic), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) are available as well. Methods are described in Iaconangelo (2026) <doi:10.5281/zenodo.22663151>.https://github.com/r-universe/cjangelo/actions/runs/35713032491Sat, 12 Sep 2026 00:51:10 GMTCLCM0.1.1successhttps://cjangelo.r-universe.devhttps://github.com/cjangelo/clcmVig_2.RmdVig_2.htmlC2 Fit Statistics2021-03-14 22:23:442026-09-12 00:51:10Vig_3.RmdVig_3.htmlCLCM with Greater than 2 Latent Classes2021-03-14 22:23:442026-09-12 00:51:10Vig_1.RmdVig_1.htmlIntroduction and Basic Examples2021-03-14 22:23:442026-09-12 00:51:10Vig_5.RmdVig_5.htmlLongitudinal CLCM & Transition Matrix2021-03-14 22:23:442026-09-12 00:51:10Vig_6.RmdVig_6.htmlLongitudinal CLCM with Latent Regression2021-03-14 22:23:442026-09-12 00:51:10Vig_7.RmdVig_7.htmlLongitudinal CLCM with Multinomial Latent Regression2021-03-14 22:23:442026-09-12 00:51:10Vig_4.RmdVig_4.htmlMultinomial Latent Regression2021-03-14 22:23:442026-09-12 00:51:10