Submitting Author Name: Marc Burri
Submitting Author Github Handle: @marcburri
Other Package Authors Github handles: (comma separated, delete if none)
Repository: https://github.com/marcburri/bridgr
Version submitted:
Submission type: Stats
Badge grade: bronze
Editor: TBD
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
- Paste the full DESCRIPTION file inside a code block below:
Package: bridgr
Type: Package
Title: Bridging Data Frequencies for Timely Economic Forecasts
Version: 0.1.2.9002
Authors@R: c(
person(
"Marc", "Burri", , "marc.burri91@gmail.com",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0001-8974-9090")
)
)
Maintainer: Marc Burri <marc.burri91@gmail.com>
Description: Implements bridge and MIDAS-style mixed-frequency models for
nowcasting and forecasting macroeconomic variables by linking
higher-frequency indicator variables to a lower-frequency target series.
The package standardizes input data, infers regular frequencies,
forecasts missing indicator observations, and aggregates indicators to
the target frequency before fitting a regression with autoregressive
target dynamics. Frequency alignment can be customized through
user-supplied conversion rules. For more on bridge and MIDAS models, see
Baffigi, A., Golinelli, R., & Parigi, G. (2004)
<doi:10.1016/S0169-2070(03)00067-0>, Ghysels, Sinko, & Valkanov (2007)
<doi:10.1080/07474930600972467>, Andreou, Ghysels, & Kourtellos (2010)
<doi:10.1016/j.jeconom.2010.01.004>, Schumacher (2016)
<doi:10.1016/j.ijforecast.2015.07.004>, and Burri (2026)
<doi:10.1111/obes.70073>.
License: MIT + file LICENSE
Encoding: UTF-8
Roxygen: list(markdown = TRUE, roclets = c("rd", "namespace", "srr::srr_stats_roclet"))
RoxygenNote: 7.3.3
LazyData: true
Imports:
dplyr,
forecast,
ggplot2,
lifecycle,
lubridate,
rlang,
scales,
tsbox,
withr
Suggests:
knitr,
rmarkdown,
srr,
testthat (>= 3.0.0),
xts
Remotes:
ropensci-review-tools/srr
Config/testthat/edition: 3
Depends:
R (>= 4.1.0)
URL: https://github.com/marcburri/bridgr, https://marcburri.github.io/bridgr/
BugReports: https://github.com/marcburri/bridgr/issues
VignetteBuilder: knitr
Additional_repositories: https://ropensci.r-universe.dev
Scope
Pre-submission Inquiry
General Information
- Who is the target audience and what are scientific applications of this package?
The target audience is applied econometricians, macroeconomists, central-bank and policy analysts, and other researchers who work with mixed-frequency time-series data and need timely nowcasts or short-horizon forecasts of lower-frequency target variables using higher-frequency indicators. Scientific applications include macroeconomic nowcasting and forecasting, business-cycle monitoring, real-time GDP tracking, and more generally any setting in which monthly, weekly, or other higher-frequency indicators are used to predict quarterly or otherwise lower-frequency outcomes.
-
Paste your responses to our General Standard G1.1 here, describing whether your software is:
- The first implementation of a novel algorithm; or
- The first implementation within R of an algorithm which has previously been implemented in other languages or contexts; or
- An improvement on other implementations of similar algorithms in R.
Please include hyperlinked references to all other relevant software.
bridgr is an improvement on other implementations of similar algorithms in R.
Bridge equations and MIDAS-style mixed-frequency regression are established methods (Baffigi, Golinelli & Parigi, 2004; Ghysels, Sinko & Valkanov, 2007; Andreou, Ghysels & Kourtellos, 2010; Schumacher, 2016), and R implementations already exist — most notably midasr, a general-purpose toolkit for estimating, testing, selecting, and forecasting MIDAS regressions, and midasml, which targets high-dimensional mixed-frequency time-series and panel models via regularization (e.g. sparse-group LASSO).
bridgr does not introduce a new estimation algorithm. Its contribution relative to these packages is a consolidated, end-to-end nowcasting workflow.
Yes
Badging
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Confirm each of the following by checking the box.
This package:
Use of Generative AI
If so, please describe usage, and include links to any relevant aspects of your repository. See our blog post for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)
Generative AI tools were used during package development and submission preparation for tasks such as drafting and revising documentation text, helping annotate and organize SRR standards, debugging continuous-integration configuration, and preparing submission materials. All resulting code, documentation, SRR annotations, and submission text were reviewed and edited by the package author before inclusion in the repository or this submission.
Publication options
Code of conduct
Submitting Author Name: Marc Burri
Submitting Author Github Handle: @marcburri
Other Package Authors Github handles: (comma separated, delete if none)
Repository: https://github.com/marcburri/bridgr
Version submitted:
Submission type: Stats
Badge grade: bronze
Editor: TBD
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which of our statistical package categories this package falls under. (Please check one or more appropriate boxes below):
Statistical Packages
Pre-submission Inquiry
General Information
The target audience is applied econometricians, macroeconomists, central-bank and policy analysts, and other researchers who work with mixed-frequency time-series data and need timely nowcasts or short-horizon forecasts of lower-frequency target variables using higher-frequency indicators. Scientific applications include macroeconomic nowcasting and forecasting, business-cycle monitoring, real-time GDP tracking, and more generally any setting in which monthly, weekly, or other higher-frequency indicators are used to predict quarterly or otherwise lower-frequency outcomes.
Paste your responses to our General Standard G1.1 here, describing whether your software is:
Please include hyperlinked references to all other relevant software.
bridgr is an improvement on other implementations of similar algorithms in R.
Bridge equations and MIDAS-style mixed-frequency regression are established methods (Baffigi, Golinelli & Parigi, 2004; Ghysels, Sinko & Valkanov, 2007; Andreou, Ghysels & Kourtellos, 2010; Schumacher, 2016), and R implementations already exist — most notably midasr, a general-purpose toolkit for estimating, testing, selecting, and forecasting MIDAS regressions, and midasml, which targets high-dimensional mixed-frequency time-series and panel models via regularization (e.g. sparse-group LASSO).
bridgr does not introduce a new estimation algorithm. Its contribution relative to these packages is a consolidated, end-to-end nowcasting workflow.
Yes
Badging
What grade of badge are you aiming for? (bronze, silver, gold)
bronze
Technical checks
Confirm each of the following by checking the box.
autotestchecks on the package, and ensured no tests fail.srr_stats_pre_submit()function confirms this package may be submitted.pkgcheck()function confirms this package may be submitted - alternatively, please explain reasons for any checks which your package is unable to pass.This package:
Use of Generative AI
If so, please describe usage, and include links to any relevant aspects of your repository. See our blog post for background. (Explicit advice is not yet included in our Dev Guide; we are hoping to update very soon, and ask your cooperation and transparency in the meantime.)
Generative AI tools were used during package development and submission preparation for tasks such as drafting and revising documentation text, helping annotate and organize SRR standards, debugging continuous-integration configuration, and preparing submission materials. All resulting code, documentation, SRR annotations, and submission text were reviewed and edited by the package author before inclusion in the repository or this submission.
Publication options
Code of conduct