kendallknight

The computational complexity of the implemented algorithm for Kendall's correlation is O(n log(n)), which is faster than the base R implementation with a computational complexity of O(n^2). For small vectors (i.e., less than 100 observations), the time difference is negligible. However, for larger vectors, the speed difference can be substantial and the numerical difference is minimal. The references are Knight (1966) doi:10.2307/2282833, Abrevaya (1999) doi:10.1016/S0165-1765(98)00255-9, Christensen (2005) doi:10.1007/BF02736122 and Emara (2024) https://learningcpp.org/. This implementation is described in Vargas Sepulveda (2024) doi:10.48550/arXiv.2408.09618.

Last 30 days

This package has been downloaded 217 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 21 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 1,100 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Nov 23, 2024 with 41 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.