Sparse Principal Component Analysis (SPCA) R package sparsepca version 0.1.2 Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>. Software Imports: rsvd N. Benjamin Erichson <erichson@uw.edu> Comprehensive R Archive Network (CRAN) N. Benjamin Erichson, Peng Zheng, and Sasha Aravkin GPL (>= 3) 2018-04-11 application/tgz https://CRAN.R-project.org/package=sparsepca doi:10.32614/CRAN.package.sparsepca