DESCRIPTION

Package: dfms
Version: 1.0.0
Title: Dynamic Factor Models
Authors@R: c(person("Sebastian", "Krantz", role = c("aut", "cre"), email = "sebastian.krantz@graduateinstitute.ch"),
             person("Rytis", "Bagdziunas", role = "aut"),
             person("Santtu", "Tikka", role = "rev"),
             person("Eli", "Holmes", role = "rev"))
Description: Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm 
  or Two-Step (2S) estimation, supporting datasets with missing data and mixed-frequency nowcasting applications. 
  Factors follow a stationary VAR process of order p. Estimation options include: running the Kalman Filter and 
  Smoother once with PCA initial values (2S) as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012>;
  iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone and Reichlin (2012) 
  <doi:10.1162/REST_a_00225>; or the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, 
  allowing arbitrary missing-data patterns and monthly-quarterly mixed-frequency datasets. The implementation uses 
  the 'Armadillo' 'C++' library and the 'collapse' package for fast estimation. A comprehensive set of methods supports 
  interpretation and visualization, forecasting, and decomposition of the 'news' content of macroeconomic data releases 
  following Banbura and Modugno (2014). Information criteria to choose the number of factors are also provided, 
  following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.
URL: https://docs.ropensci.org/dfms/, https://github.com/ropensci/dfms
BugReports: https://github.com/ropensci/dfms/issues
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.1), collapse (>= 2.0.0)
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: 7.3.2
Config/testthat/edition: 3
VignetteBuilder: knitr