Changes in version 0.2.0.9000                      

                 Changes in version 0.2.0 (2026-01-31)                  

Fast Additive Switching of Seasonality, Trend and Exogenous Regressors
(FASSTER) is a state space model designed for forecasting time series
with multiple seasonal patterns. The model extends traditional state
space models by introducing a switching component to the measurement
equation, enabling flexible modeling of complex seasonal patterns, and
time series dynamics with rapid structural changes.

  - FASSTER model implementation:

  - Model specification: Flexible formula interface supporting:
    
      - trend() for polynomial trends
      - season() for seasonal factors
      - fourier() for trigonometric seasonal terms
      - ARMA() for autoregressive moving average components
      - xreg() for exogenous regressors
      - %S% switching operator for group-specific model structures
      - %?% conditional operator for time-varying components

  - Model methods: Full integration with the fable framework:
    
      - fitted() and residuals() for model diagnostics
      - augment() for augmenting data with model estimates
      - tidy() for extracting coefficients (initial state estimates)
      - glance() for model summary statistics (AIC, BIC, log-likelihood)
      - report() for displaying estimated state and observation
        variances
      - components() for decomposing fitted values into trend and
        seasonal components
      - forecast() for generating predictions
      - interpolate() for filling missing values
      - refit() for applying a fitted model to new data with optional
        re-estimation
      - stream() for extending models with new observations

  - Heuristic estimation: Model parameters are estimated using a
    heuristic approach based on filtering and smoothing to obtain
    initial state parameters and variance estimates.