FourWayHMM

Implements parsimonious hidden Markov models for four-way data via expectation-conditional maximization algorithm, as described in Tomarchio et al. (2020) doi:10.48550/arXiv.2107.04330. The matrix-variate normal distribution is used as emission distribution. For each hidden state, parsimony is reached via the eigen-decomposition of the covariance matrices of the emission distribution. This produces a family of 98 parsimonious hidden Markov models.

Last 30 days

This package has been downloaded 232 times in the last 30 days. Now we're getting somewhere! Enough downloads to populate a lively group chat. The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 4 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 3,209 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 23, 2024 with 32 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.