Estimate Dynamic Factor Models with Sparse Loadings R package sparseDFM version 1.0 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'. Software Depends: R (>= 3.3.0) Imports: Rcpp (>= 1.0.9), Matrix, ggplot2 LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, gridExtra Alex Gibberd <a.gibberd@lancaster.ac.uk> Comprehensive R Archive Network (CRAN) Luke Mosley [aut], Tak-Shing Chan [aut], Alex Gibberd [aut, cre] GPL (>= 3) 2023-03-23 application/tgz https://CRAN.R-project.org/package=sparseDFM doi:10.32614/CRAN.package.sparseDFM