admix

Implements techniques to estimate the unknown quantities related to two-component admixture models, where the two components can belong to any distribution (note that in the case of multinomial mixtures, the two components must belong to the same family). Estimation methods depend on the assumptions made on the unknown component density; see Bordes and Vandekerkhove (2010) doi:10.3103/S1066530710010023, Patra and Sen (2016) doi:10.1111/rssb.12148, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) doi:10.3150/23-BEJ1593. In practice, one can estimate both the mixture weight and the unknown component density in a wide variety of frameworks. On top of that, hypothesis tests can be performed in one and two-sample contexts to test the unknown component density (see Milhaud, Pommeret, Salhi and Vandekerkhove (2022) doi:10.1016/j.jspi.2021.05.010, and Milhaud, Pommeret, Salhi, Vandekerkhove (2024) doi:10.3150/23-BEJ1593). Finally, clustering of unknown mixture components is also feasible in a K-sample setting (see Milhaud, Pommeret, Salhi, Vandekerkhove (2024) https://jmlr.org/papers/v25/23-0914.html).

Last 30 days

This package has been downloaded 627 times in the last 30 days. Not bad! The download count is somewhere between 'small-town buzz' and 'moderate academic conference'. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 8 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 6,809 times in the last 365 days. That's a lot of interest! Someone might even write a blog post about it. The day with the most downloads was Nov 20, 2024 with 83 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.