https://choxos.r-universe.devRecent package updates in choxosR-universehttps://github.com/choxos.pnghttps://choxos.r-universe.devWed, 20 May 2026 11:27:19 GMTa.sofimahmudi@gmail.com (Ahmad Sofi-Mahmudi)Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, and count outcomes via 'Stan'. Also provides simulated treatment comparison (STC) via parametric G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the ML-NMR methodology (Phillippo et al. 2020, <doi:10.1111/rssa.12579>) implemented in the 'multinma' package (GPL-3) to the unanchored two-trial case; the public API deliberately mirrors multinma's so users can move between ML-NMR and ML-UMR with the same workflow.https://github.com/r-universe/choxos/actions/runs/26768497132Wed, 20 May 2026 11:27:19 GMTmlumr0.1.0successhttps://choxos.r-universe.devhttps://github.com/choxos/mlumrmodel-comparison.Rmdmodel-comparison.htmlComparing ML-UMR, STC, and Naive Methods2026-05-15 17:57:572026-05-15 17:57:57data-preparation.Rmddata-preparation.htmlData Preparation and Integration2026-05-15 17:57:572026-05-15 17:57:57mlumr-models.Rmdmlumr-models.htmlFitting ML-UMR Models2026-05-15 17:57:572026-05-15 17:57:57introduction.Rmdintroduction.htmlIntroduction to mlumr2026-05-15 17:57:572026-05-15 17:57:57stc-and-naive.Rmdstc-and-naive.htmlSTC and Naive Benchmarks2026-05-15 17:57:572026-05-15 17:57:57worked-example.Rmdworked-example.htmlWorked Example: Complete Analysis2026-05-15 17:57:572026-05-15 17:57:57a.sofimahmudi@gmail.com (Ahmad Sofi-Mahmudi)Bayesian multilevel unanchored meta-regression (ML-UMR) for indirect treatment comparisons using individual patient data (IPD) and aggregate data (AgD). Implements shared prognostic factor assumption (SPFA) and relaxed SPFA models for binary, continuous, and count outcomes via 'Stan'. Also provides simulated treatment comparison (STC) via parametric G-computation and naive unadjusted benchmarks. ML-UMR is an adaptation of the ML-NMR methodology (Phillippo et al. 2020, <doi:10.1111/rssa.12579>) implemented in the 'multinma' package (GPL-3) to the unanchored two-trial case; the public API deliberately mirrors multinma's so users can move between ML-NMR and ML-UMR with the same workflow.https://github.com/r-universe/cran/actions/runs/26166581131Wed, 20 May 2026 08:30:02 GMTmlumr0.1.0successhttps://cran.r-universe.devhttps://github.com/cran/mlumrmodel-comparison.Rmdmodel-comparison.htmlComparing ML-UMR, STC, and Naive Methods2026-05-20 08:30:022026-05-20 08:30:02data-preparation.Rmddata-preparation.htmlData Preparation and Integration2026-05-20 08:30:022026-05-20 08:30:02mlumr-models.Rmdmlumr-models.htmlFitting ML-UMR Models2026-05-20 08:30:022026-05-20 08:30:02introduction.Rmdintroduction.htmlIntroduction to mlumr2026-05-20 08:30:022026-05-20 08:30:02stc-and-naive.Rmdstc-and-naive.htmlSTC and Naive Benchmarks2026-05-20 08:30:022026-05-20 08:30:02worked-example.Rmdworked-example.htmlWorked Example: Complete Analysis2026-05-20 08:30:022026-05-20 08:30:02