bigmds

MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) doi:10.48550/arXiv.2007.11919 (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) doi:10.48550/arXiv.2007.11919 (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).

Last 30 days

This package has been downloaded 291 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 18 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 3,857 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 Jul 21, 2024 with 76 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.