Robust Panel Clustering Algorithm R package RobPC version 1.4 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>. Software Depends: R (>= 4.0) Imports: stats, trimcluster Hasan Bulut <hasan.bulut@omu.edu.tr> Comprehensive R Archive Network (CRAN) Hasan Bulut [aut, cre] GPL-2 2025-02-20 application/tgz https://CRAN.R-project.org/package=RobPC doi:10.32614/CRAN.package.RobPC