Table 2. Overview of sparse principal component analysis (sPCA) methods used.

KSS: Karlis-Saporta-Spinaki criterion. Package: R package implementation; Features: short description of the method; Choice: method of selection of the number of informative components in real data; PCs: number of informative PCs.

Method	Package	Authors	Features	Choice	PCs
RSPCA	pcaPP	Croux et al., 2013	Robust sPCA (RSPCA), different measure of dispersion ($Q_n$)	Permutation KSS	6
SFPCA	Code in publication, Supplementary Material	Guo et al., 2010	Fused penalties for block correlation	KSS	6
sPCA	elasticnet	Zou et al., 2006	Formulation of sPCA as a regression problem	KSS	6
SCA	SCA	Chen and Rohe, 2021	Rotation of eigen vectors for approximate sparsity	Permutation KSS	6