MacroFilters: Robust Trend-Cycle Decomposition for Macroeconomic Time Series

Provides high-performance tools for macroeconomic trend extraction and filtering, specifically designed to solve the end-point problem in real-time. Implements the MacroBoost Hybrid (MBH) filter using penalized P-splines and gradient boosting. Unlike the standard Hodrick-Prescott filter, 'MacroFilters' utilizes component-wise L2-boosting with robust loss functions (Huber) to handle extreme transient shocks (e.g., COVID-19) without inducing spurious trend shifts. The algorithm includes an automated two-layer diagnostic stage for unit roots and structural breaks, optimized via corrected AICc for computational efficiency. Methodology detailed in Kinel (2026) <doi:10.2139/ssrn.6371138>.

Version:	0.2.1
Depends:	R (≥ 3.5)
Imports:	data.table, ggplot2, Matrix, mboost, tseries
Suggests:	knitr, rmarkdown, scales, strucchange, testthat (≥ 3.0.0), usethis, xts, zoo
Published:	2026-06-11
DOI:	10.32614/CRAN.package.MacroFilters
Author:	Michal Kinel [aut, cre]
Maintainer:	Michal Kinel <michal.kinel at gmail.com>
BugReports:	https://github.com/michal0091/MacroFilters/issues
License:	MIT + file LICENSE
URL:	https://github.com/michal0091/MacroFilters, https://michal0091.github.io/MacroFilters/
NeedsCompilation:	no
Language:	en-US
Citation:	MacroFilters citation info
Materials:	README, NEWS
CRAN checks:	MacroFilters results

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