Changes in version 2.0.0 (2026-09-01)                  

Breaking changes

  - Internal computation no longer relies on xts/zoo. Series used during
    estimation and filtering are now represented with a new,
    dependency-free idx_series class, with calendar handling provided by
    a companion idx_calendar class. Bundled datasets are unaffected and
    remain xts objects; xts_to_idx() is provided to convert
    user-supplied xts/zoo data to idx_series where needed. Code that
    relied on the internal (non-exported) representation of series may
    need to be updated.

  - SSModelDynGompertzReinit has been deprecated. Its functionality is
    now provided directly by SSModelDynamicGompertz via the reinit.idx,
    original.results, and use.presample.info fields, so a single class
    now covers both the standard and reinitialised model. SSModelBase
    has also been deprecated, with its logic folded into
    SSModelDynamicGompertz.

  - 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.

                  Changes in version 0.0 (2024-08-26)                   

  - Added a NEWS.md file to track changes to the package.