Changes in version 2.0.0                        

Initial Release

  - Implements Residual-Based Fully Modified VAR (RBFM-VAR) estimator
    following Chang (2000).

  - Core estimation:
    
      - rbfmvar(): Main estimation function for RBFM-VAR models.
      - Handles unknown mixtures of I(0), I(1), and I(2) variables.
      - FM+ bias correction for asymptotically valid inference.

  - Lag selection:
    
      - Automatic lag selection via AIC, BIC, or HQ information
        criteria.
      - ic_table(): Display information criteria comparison.

  - Long-run variance estimation:
    
      - Bartlett (Newey-West), Parzen, and Quadratic Spectral kernels.
      - Andrews (1991) automatic bandwidth selection.

  - Inference:
    
      - granger_test(): Granger non-causality testing with modified Wald
        statistics.
      - granger_matrix(): Pairwise Granger causality tests.
      - Asymptotically chi-squared inference regardless of integration
        orders.

  - Impulse response analysis:
    
      - irf(): Orthogonalized impulse response functions.
      - Bootstrap confidence intervals via Kilian (1998) method.

  - Forecast error variance decomposition:
    
      - fevd(): Cholesky-identified variance decomposition.

  - Forecasting:
    
      - forecast(): Out-of-sample forecasting with prediction intervals.

  - Methods:
    
      - print(), summary(), plot() methods for all major objects.
      - coef(), residuals(), fitted(), vcov() extractors.

References

Chang, Y. (2000). Vector Autoregressions with Unknown Mixtures of I(0),
I(1), and I(2) Components. Econometric Theory, 16(6), 905-926.
doi:10.1017/S0266466600166071