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: 2026-07-08 07:54:21 UTC; root
Author: Luke Mosley [aut], Tak-Shing Chan [aut], Alex Gibberd [aut,
        cre]
Maintainer: Alex Gibberd <a.gibberd@lancaster.ac.uk>
Repository: https://cran.r-universe.dev
Date/Publication: 2023-03-23 18:40:02 UTC
RemoteUrl: https://github.com/cran/sparseDFM
RemoteRef: HEAD
RemoteSha: e6438f1804463a0bb18bcfd97cba909418cd283e