BayesMultiMode: Bayesian Mode Inference
A Bayesian approach for mode inference which works in two steps. First, a mixture distribution is
fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo
(MCMC) algorithm following Malsiner-Walli, Frühwirth-Schnatter and Grün (2016)
<doi:10.1007/s11222-014-9500-2>). The number of mixture components does not have
to be known; the size of the mixture is estimated endogenously through the SFM
approach. Second, the modes of the estimated mixture at each MCMC draw are retrieved
using algorithms specifically tailored for mode detection. These estimates are then
used to construct posterior probabilities for the number of modes, their locations
and uncertainties, providing a powerful tool for mode inference.
| Version: |
0.6.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
assertthat, bayesplot, dplyr, ggplot2, ggpubr, gtools, magrittr, MCMCglmm, mvtnorm, posterior, sn, stringr, tidyr, Rdpack, scales |
| Suggests: |
testthat (≥ 3.0.0) |
| Published: |
2023-08-08 |
| Author: |
Nalan Baştürk [aut],
Jamie Cross [aut],
Peter de Knijff [aut],
Lennart Hoogerheide [aut],
Paul Labonne [aut, cre],
Herman van Dijk [aut] |
| Maintainer: |
Paul Labonne <paul.labonne at bi.no> |
| BugReports: |
https://github.com/paullabonne/BayesMultiMode/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/paullabonne/BayesMultiMode |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
BayesMultiMode results |
Documentation:
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