tsgc

• In FilterResults, the confidence_level argument to predict_all() and get_gy_ci() has been renamed to confidence.level, for consistency with naming elsewhere in the package. Calls that pass this argument by name will need to be updated. The default value of sea.on in predict_all() has also changed, from FALSE to TRUE.
• forecast_peak(), forecast.peak(), and plot_new_cases() have been deprecated. plot_new_cases() has been renamed to plot_forecast() as the growth curve theory extends beyond epidemiological settings.

New features

• Added a leading indicator model, via the new SSModelLeadingIndicator and FilterResultsLI reference classes, for modelling a target series using a leading series (see Harvey, A. (2021). "Time Series Modelling of Epidemics: Leading Indicators, Control Groups and Policy Assessment." *National Institute Economic Review*, 257, 83-100). FilterResultsLI provides predict_level(), predict_all(), get_growth_y(), get_gy_ci(), print(), summary(), and mapes() methods, paralleling FilterResults for the standard model.
• Added five new example datasets: ukitaly, gauteng_weather_2021, england_weather_2021, nintendo_sales, and etrading_apps, including weather data to support leading indicator examples.
• Added idx_series() and supporting methods (idx_cbind(), idx_rbind(), idx_diff(), idx_lag(), idx_range(), idx_values(), idx_positions(), and standard methods including length(), head(), tail(), as.matrix(), as.numeric(), as.double(), and print()) as a dependency-free representation of time series for internal computation.
• Added idx_calendar() and supporting helpers (idx_step(), idx_to_date(), idx_calendar_offset(), idx_calendar_step(), idx_calendar_multi_step(), and related utilities) providing a consistent translation layer between integer positions and calendar dates for daily, weekly, monthly, quarterly, and annual data.
• Added get_timeframe(), idx_to_pos(), idx_offset_to_pos(), and idx_detect_calendar_pattern() to support the new indexing system.
• Added print_model_diagnostics() to surface model diagnostics more clearly.
• Added xts_to_idx() to convert xts/zoo objects to the internal idx_series representation, for users supplying their own xts data.
• Added cross_val() for cross-validating one or more models over a series of rolling estimation windows, and mapes() for computing mean absolute percentage errors from forecast results.
• Added df2ldl_lead() for handling two-column data for leading indicator models.
• Added print() and summary() S3 methods for SSModelDynamicGompertz, SSModelLeadingIndicator, FilterResults, and FilterResultsLI.
• Added new plotting functions plot_compare_forecast() (compare forecasts across models), plot_log_forecast() (forecasts on the log scale), and plot_r0() (plot estimated reproduction numbers from estimate_r0()). Also added plotting functionality for models (plot() takes in a model object)
• Added a new set of accessor functions (accessorFns.R) for extracting components from fitted model and KFS objects, including output(), modelKFS(), seasonalComp(), att(), Ptt(), get_V(), gety(), alphahat(), and a standalone estimate() wrapper around a model's estimate() method.

Improvements

• Substantially expanded plotting internals with helper functions (idx_series_df(), idx_resolve_axis(), idx_axis_opts(), idx_x_scale(), idx_x_lab(), idx_add_info_box(), and related helpers) to give more consistent, better-labelled axes across all plot types, including support for non-daily frequencies.
• Reduced the package's dependency footprint by removing reliance on xts/zoo from internal computation.

Bug fixes

• Various small fixes to filtering, forecasting, and plotting uncovered during the internal rewrite.