changepoints

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: doi:10.1214/20-EJS1710, doi:10.48550/arXiv.2006.03283; 2) univariate polynomials: doi:10.1214/21-EJS1963; 3) univariate and multivariate nonparametric settings: doi:10.1214/21-EJS1809, doi:10.1109/TIT.2021.3130330; 4) high-dimensional covariances: doi:10.3150/20-BEJ1249; 5) high-dimensional networks with and without missing values: doi:10.1214/20-AOS1953, doi:10.48550/arXiv.2101.05477, doi:10.48550/arXiv.2110.06450; 6) high-dimensional linear regression models: doi:10.48550/arXiv.2010.10410, doi:10.48550/arXiv.2207.12453; 7) high-dimensional vector autoregressive models: doi:10.48550/arXiv.1909.06359; 8) high-dimensional self exciting point processes: doi:10.48550/arXiv.2006.03572; 9) dependent dynamic nonparametric random dot product graphs: doi:10.48550/arXiv.1911.07494; 10) univariate mean against adversarial attacks: doi:10.48550/arXiv.2105.10417.

Last 30 days

This package has been downloaded 320 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 2 times.

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Last 365 days

This package has been downloaded 3,359 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Aug 22, 2024 with 29 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.