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Changelog

Source: NEWS.md

dfms 1.0.0

CRAN release: 2026-01-26

  • dfms is now part of rOpenSci – following completion of a scientific review by @eeholmes and @santikka. This means the repo shifted to ropensci/dfms and the docs (now in rOpenSci style) to docs.ropensci.org/dfms.

  • Added a news() function for news decomposition identifying the contribution of new data releases to DFM predictions/nowcasts following research codes by Banbura and Modugno (2014). It supports efficient multi-target news estimation, has been tested with MQ/AR(1) model versions, and is suitable for mixed-frequency nowcasting applications.

  • Added arguments save.full.state to DFM() and use.full.state to predict(), fitted(), and residuals() - default TRUE. Full-state output includes idiosyncratic components (when modeled with idio.ar1 = TRUE). This generally improves the accuracy of DFM forecasts, but changes the interpretation for residuals/fitted values, which are then no longer a function of the factors alone.

  • Marked 1.0.0 as the package is now peer-reviewed and feature-complete within the intended scope: the full and efficient implementation of Banbura and Modugno (2014) in R.

dfms 0.4.0

CRAN release: 2026-01-18

  • Added support for mixed-frequency estimation with AR(1) idiosyncratic errors (quarterly.vars combined with idio.ar1 = TRUE). This implements the full model of Banbura and Modugno (2014), allowing observation errors to follow AR(1) processes while handling mixed monthly-quarterly data with temporal aggregation constraints.

  • New internal functions init_cond_MQ_idio() and EMstepBMMQidio() implement the EM algorithm for the combined MQ + idio.ar1 case, with state vector structure [factors, monthly_errors, quarterly_error_lags].

  • Updated plot.dfm() with type = "residual" to properly handle mixed-frequency and AR(1) error models by using the residuals() method internally.

  • Added examples and documentation for the new MQ + idio.ar1 functionality in both the DFM() help page and the introductory vignette.

dfms 0.3.2

CRAN release: 2025-09-24

  • Minor internal C++ changes to ensure compatibility with RcppArmadillo 15.0.2.

dfms 0.3.1

CRAN release: 2025-08-20

  • Fixed bug which occurred with only one quarterly variable (#73). Thanks @SantiagoD999 for reporting.

dfms 0.3.0

CRAN release: 2025-05-18

  • Added argument quarterly.vars, enabling mixed-frequency estimation with monthly and quarterly data following Banbura and Modugno (2014). The data matrix should contain the quarterly variables at the end (after the monthly ones).

dfms 0.2.2

CRAN release: 2024-06-09

  • Replace Armadillo inv_sympd() by Armadillo inv() in C++ Kalman Filter to improve numerical robustness at a minor performance cost.

dfms 0.2.1

CRAN release: 2023-04-03

  • Fixed print bug in summary.dfm: print method showed that model had AR(1) errors even though idio.ar1 = FALSE by default.

dfms 0.2.0

CRAN release: 2023-03-31

  • Added argument idio.ar1 = TRUE allowing estimation of approximate DFM’s with AR(1) observation errors.

  • Added a small theoretical vignette entitled ‘Dynamic Factor Models: A Very Short Introduction’. This vignette lays a foundation for the present and future functionality of dfms. I plan to implement all features described in this vignette until summer 2023.

dfms 0.1.5

  • Added argument na.keep = TRUE to fitted.dfm. Setting na.keep = FALSE allows interpolation of data based on the DFM. Thanks @apoorvalal (#45).

dfms 0.1.4

CRAN release: 2023-01-12

  • Fixed minor bug in summary.dfm occurring if only one factor was estimated (basically an issue with dropping matrix dimensions which lead the factor summary statistics to be displayed without names).

dfms 0.1.3

CRAN release: 2022-10-12

  • Implemented some minor CRAN comments, no changes to functionality.

dfms 0.1.2

  • New default em.method = "auto", which uses "BM" if the data has any missing values and "DGR" otherwise.

  • Added vignette providing a walkthrough of the main features.

dfms 0.1.1

  • Renamed package from DFM to dfms. Lowercase names are preferred by rOpenSci, and this also helps distinguish the package name from the main function DFM(). The new name was inspired by the vars package.