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cff-version: 1.2.0
message: 'To cite package "dfms" in publications use:'
type: software
license: GPL-3.0-only
title: 'dfms: Dynamic Factor Models'
version: 1.0.0
doi: 10.32614/CRAN.package.dfms
abstract: '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) <https://doi.org/10.1016/j.jeconom.2011.02.012>;
  iterated Kalman Filtering and Smoothing until EM convergence as in Doz, Giannone
  and Reichlin (2012) <https://doi.org/10.1162/REST_a_00225>; or the adapted EM algorithm
  of Banbura and Modugno (2014) <https://doi.org/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) <https://doi.org/10.1111/1468-0262.00273>.'
authors:
- family-names: Krantz
  given-names: Sebastian
  email: sebastian.krantz@graduateinstitute.ch
- family-names: Bagdziunas
  given-names: Rytis
repository: https://ropensci.r-universe.dev
repository-code: https://github.com/ropensci/dfms
commit: ba1af5a03deecd79fe68dbdc5f839e9120c587eb
url: https://docs.ropensci.org/dfms/
date-released: '2026-01-27'
contact:
- family-names: Krantz
  given-names: Sebastian
  email: sebastian.krantz@graduateinstitute.ch