RobPC: Robust Panel Clustering Algorithm
Performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <doi:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.
| Version: |
1.4 |
| Depends: |
R (≥ 4.0) |
| Imports: |
stats, trimcluster |
| Published: |
2025-02-20 |
| Author: |
Hasan Bulut [aut, cre] |
| Maintainer: |
Hasan Bulut <hasan.bulut at omu.edu.tr> |
| License: |
GPL-2 |
| NeedsCompilation: |
no |
| CRAN checks: |
RobPC results |
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