Evidence-Based Bayesian Disaggregation of Aggregate Indices R package BayesianDisaggregation version 0.2.1 Disaggregates an observed aggregate price index into sectoral components with a Bayesian state-space model in which the aggregate enters as a genuine observation density rather than as a renormalization identity. A random-walk-with-drift transition in log space (with partial pooling on the drift and the innovation scale) and an estimable cross-sectional concentration produce posterior draws of the sectoral indices with credible intervals, suitable as multiple-imputation input for downstream dynamic models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al., 2017) <doi:10.18637/jss.v076.i01>; model comparison uses Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and Gabry, 2017) <doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the same aggregate evidence. Software Depends: R (>= 4.1.0) Imports: readxl, dplyr, tidyr, stringr, magrittr, stats, parallel Suggests: cmdstanr, rstan (>= 2.21.0), posterior, loo (>= 2.5.0), knitr, rmarkdown, ggplot2, readr, testthat (>= 3.0.0) José Mauricio Gómez Julián <isadore.nabi@pm.me> Comprehensive R Archive Network (CRAN) José Mauricio Gómez Julián [aut, cre] (ORCID: <https://orcid.org/0009-0000-2412-3150>) MIT + file LICENSE (https://CRAN.R-project.org/package=BayesianDisaggregation/LICENSE) 2026-06-18 application/tgz https://CRAN.R-project.org/package=BayesianDisaggregation doi:10.32614/CRAN.package.BayesianDisaggregation