Changelog

Source: NEWS.md

bridgr (development version)

• Fix ragged-edge completion for sub-monthly indicators at multi-step horizons (h > 1). Completion previously filled future target periods with a fixed count of high-frequency grid steps, but calendar periods can hold more observations than the regular ladder implies (a quarter has 13 weekly or up to 92 daily observations versus the 12 or 84 the ladder expects), so early future periods absorbed the surplus and later ones failed block validation. Completion is now period-aware: candidate grid times are assigned to their calendar periods and each future period receives exactly the observations it still needs.

• Ignore indicator observations dated beyond the last forecast period during alignment. Such observations cannot enter any regressor and previously made block validation fail on a partially observed beyond-horizon period, for example when a weekly series extends past the target quarter of an h = 1 nowcast.

• Make direct alignment (indic_predict = "direct") period-aware. Blocks of high-frequency observations were strided backward from the end of the sample and paired with target periods by position, so on calendar ladders (13-Saturday quarters on a 12-slot weekly ladder) historical blocks drifted out of their calendar periods – about one week per quarter, compounding over the sample – and the stride count could overrun the number of target periods and fail outright. Each target period that overlaps the observed sample is now anchored at the newest observation’s position within its own period (a MIDAS-with-leads alignment), which reproduces fixed strides exactly on regular ladders; periods beyond the observed sample keep the documented lead convention.

• 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.