BayesMultiMode

A two-step Bayesian approach for mode inference following Cross, Hoogerheide, Labonne and van Dijk (2024) doi:10.1016/j.econlet.2024.111579. First, a mixture distribution is fitted on the data using a sparse finite mixture (SFM) Markov chain Monte Carlo (MCMC) algorithm. 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.

Last 30 days

This package has been downloaded 572 times in the last 30 days. This could be a paper that people cite without reading. Reaching the medium popularity echelon is no small feat! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 50 times.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.

Last 365 days

This package has been downloaded 5,259 times in the last 365 days. A solid achievement! Enough downloads to get noticed at department meetings. The day with the most downloads was Feb 20, 2025 with 61 downloads.

The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.