ktaucenters
Robust Clustering Procedures
A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) doi:10.48550/arXiv.1906.08198).
- Version1.0.0
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- Languageen-US
- Last release01/16/2024
Documentation
Team
Juan Domingo Gonzalez
Victor J. Yohai
Show author detailsRolesAuthorRuben H. Zamar
Show author detailsRolesAuthorDouglas Alberto Carmona Guanipa
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 139 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 1 times.
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Last 365 days
This package has been downloaded 2,201 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 23, 2024 with 30 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.
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Dependencies
- Depends2 packages
- Imports1 package
- Suggests5 packages
- Linking To1 package
- Reverse Imports1 package