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
DOI:	10.32614/CRAN.package.RobPC
Author:	Hasan Bulut [aut, cre]
Maintainer:	Hasan Bulut <hasan.bulut at omu.edu.tr>
License:	GPL-2
NeedsCompilation:	no
CRAN checks:	RobPC results

Documentation:

Reference manual:

RobPC.pdf

Downloads:

Package source:	RobPC_1.4.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: RobPC_1.4.zip
macOS binaries:	r-devel (arm64): RobPC_1.4.tgz, r-release (arm64): RobPC_1.4.tgz, r-oldrel (arm64): RobPC_1.4.tgz, r-devel (x86_64): RobPC_1.4.tgz, r-release (x86_64): RobPC_1.4.tgz, r-oldrel (x86_64): RobPC_1.4.tgz

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