Changelog

Source: NEWS.md

bridgr (development version)

• Rename the main model-construction entry point to mf_model(), rename the fitted-model class and S3 methods to mf_model, and keep bridge() as a deprecated compatibility wrapper.

• Extend mf_model() beyond classic bridge aggregation:

  • add unrestricted mixed-frequency regressors via indic_aggregators = "unrestricted"
  • add parametric "beta" weighting alongside "expalmon"
  • add direct high-frequency alignment via indic_predict = "direct"
  • support fixed numeric aggregation weights supplied in a list()

• Improve mixed-frequency input handling:

  • infer regular frequencies from second through year
  • allow custom frequency_conversions
  • standardize month-, quarter-, and year-end dates to period starts when needed for frequency recognition
  • keep the most recent observations in overfilled target periods with a summarized warning
  • fail when target periods contain too few high-frequency observations

• Add joint parametric aggregation optimization controls through solver_options, including optimizer choice, multi-start runs, seeds, iteration limits, and user-supplied starting values.

• Add uncertainty support:

  • se = TRUE for coefficient uncertainty and prediction intervals
  • HAC standard errors for linear bridge equations
  • Delta-HAC standard errors when parametric aggregation weights are estimated jointly
  • residual-resampling prediction intervals by default
  • optional full-system block bootstrap uncertainty via full_system_bootstrap = TRUE

• Add scenario forecasting support in forecast.mf_model() through custom future xreg paths and standardized forecast objects with uncertainty metadata.

• Add plotting methods and helpers:

  • plot.mf_model() for fit and forecast plots
  • theme_bridgr(), colors_bridgr(), scale_color_bridgr(), and scale_fill_bridgr()

• Expand printed output and documentation:

  • standardize summary.mf_model() and forecast.mf_model() output
  • add vignettes on mixed-frequency modeling, ragged-edge nowcasting, and uncertainty / scenario analysis
  • refresh the README examples and package references

• Remove the legendre parametric aggregation option.

• Use analytic gradients for expalmon optimization and improve the normalized beta polynomial gradient used in the optimizer.

bridgr 0.1.2

CRAN release: 2026-02-18

• Solve dependency issues with xts

bridgr 0.1.1

CRAN release: 2024-12-13

• Initial CRAN submission:

  • Added gdp,baro, wea and fcurve datasets.

  • Added bridge(), forecast() and summary() functions.

  • Supports target variables on monthly, quarterly and yearly frequency, and indicator variables on daily, weekly, monthly, quarterly and yearly frequency.

  • Supports auto.arima, ets and other methods for indicator variable forecasting.

  • Supports aggregation of indicator variables to match the target’s frequency using custom weighting functions, exponential Almon polynomials and other methods.