DESCRIPTION

Package: dfms
Version: 0.2.2
Title: Dynamic Factor Models
Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"),
             person("Rytis", "Bagdziunas", role = "aut"))
Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the 
  econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 
  2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated 
  Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) 
  <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
  allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and 
  the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports 
  interpretation and visualization of the model as well as forecasting. Information criteria to choose the number 
  of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
URL: https://sebkrantz.github.io/dfms/
BugReports: https://github.com/SebKrantz/dfms/issues
Depends: R (>= 3.3.0)
Imports: Rcpp (>= 1.0.1), collapse (>= 1.8.0)
LinkingTo: Rcpp, RcppArmadillo
Suggests: xts, vars, magrittr, testthat (>= 3.0.0), knitr, rmarkdown, covr
License: GPL-3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd", "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3
Config/testthat/edition: 3
VignetteBuilder: knitr