Package: sparseDFM
Title: Estimate Dynamic Factor Models with Sparse Loadings
Version: 1.0
Authors@R: 
    c(person(given = "Luke",
           family = "Mosley",
           role = c("aut"),
           email = "l.mosley@lancaster.ac.uk"),
      person(given = "Tak-Shing",
           family = "Chan",
           role = c("aut"),
           email = "t.t.chan@lancaster.ac.uk"),
      person(given = "Alex",
           family = "Gibberd",
           role = c("aut", "cre"),
           email = "a.gibberd@lancaster.ac.uk")
      )
Description: 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'.   
License: GPL (>= 3)
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: Rcpp (>= 1.0.9), Matrix, ggplot2
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, gridExtra
VignetteBuilder: knitr
Depends: R (>= 3.3.0)
LazyData: true
NeedsCompilation: yes
Packaged: 2023-03-23 10:36:30 UTC; mosleyl
Author: Luke Mosley [aut],
  Tak-Shing Chan [aut],
  Alex Gibberd [aut, cre]
Maintainer: Alex Gibberd <a.gibberd@lancaster.ac.uk>
Repository: CRAN
Date/Publication: 2023-03-23 19:40:02 UTC
Built: R 4.6.1; x86_64-w64-mingw32; 2026-07-21 02:49:49 UTC; windows
Archs: x64
