                        Changes in version 0.2.0                        

New features

  - Seasonal models. fit_modal_arima() gains a seasonal argument, with
    the same form as in stats::arima(), to fit Modal
    SARIMA(p,d,q)(P,D,Q)[s] models. The seasonal coefficients are
    reported as sar1, sma1, and so on. Existing non-seasonal calls are
    unchanged.
  - auto.modal.arima() searches over seasonal orders (max.P, max.Q),
    chooses the seasonal differencing order D with forecast::nsdiffs(),
    and gains a trace argument.
  - forecast() gains point = c("joint", "marginal"). The default,
    "joint", is the most probable future trajectory, as in 0.1.0.
    "marginal" gives the conditional mode of each future value, which
    differs from the joint path for skewed errors and horizons greater
    than one. The returned object also contains mode_path and
    mode_shift.
  - Asymptotic prediction intervals now use the exact distribution of
    the forecast error at the estimated parameters, computed by
    numerical convolution of the SKD densities.
  - New vcov() and nobs() methods. summary() reports a test of symmetry,
    H0: gamma = 1.
  - diagnostics() and envelope() support seasonal models; for these the
    residual ACF, PACF and Ljung-Box test cover two seasonal periods.
  - envelope() gains refit = TRUE (the default): each replication
    simulates a series from the fitted model and re-estimates it, so the
    envelope accounts for parameter estimation. Without it, the smallest
    distances of a correct Skewed Laplace fit fall below the envelope,
    because its maximum likelihood fit pulls several residuals to zero.
    refit = FALSE keeps the faster envelope based on simulated
    innovations.
  - Sergio Luis Mercado Londoño and Víctor Hugo Lachos join as
    contributors.

Changes in parameterization

  - The Skew-Normal now uses the parameterization of Galarza et al.
    (2017), f(y) = 4p(1-p)/sqrt(2*pi*sigma^2) *
    exp(-2*rho_p((y-mu)/sigma)^2), the same as the Skewed Student-t and
    Skewed Laplace. sigma therefore has the same meaning for the three
    distributions. For Skew-Normal fits, the log-likelihood, the
    systematic coefficients and gamma are unchanged; the reported sigma
    equals the 0.1.0 value divided by (gamma + 1/gamma)/2.
  - BIC() and logLik() use the number of observations after
    differencing.

Bug fixes

  - The analytical gradients of the Skewed Student-t and Skewed Laplace
    log-likelihoods had errors, so the optimizer could stop far from the
    maximum while reporting convergence. For example, a Skewed Laplace
    ARIMA(2,1,1) for log10(lynx) reached a log-likelihood of -189.99
    in 0.1.0 and -0.14 now. Estimates from these two distributions may
    change substantially.
  - The Skewed Laplace fit is refined with Nelder-Mead after BFGS,
    because its log-likelihood is not differentiable when a residual is
    zero, and its standard errors use the outer product of the scores
    instead of the Hessian.
  - Standard errors of sigma, gamma and nu in summary() were those of
    their logarithms; they now use the delta method.
  - The random generators used for bootstrap intervals and envelopes did
    not match the fitted Student-t and Laplace densities (the Laplace
    draws were half as wide even for gamma = 1). All draws now come from
    the fitted density.
  - The Skewed Laplace CDF was wrong below the mode, which affected the
    lower asymptotic prediction bounds and the randomized quantile
    residuals.
  - The theoretical quantiles of the Laplace envelope were half their
    correct value.
  - Removed an unrelated script, inst/extras/devTests.R.

                 Changes in version 0.1.0 (2026-05-12)                  

  - Initial release.
  - Added fit_modal_arima() for fitting Parametric Modal ARIMA models
    using the generalized SKD family.
  - Implemented S3 methods: print, coef, AIC, BIC, summary, and plot.
  - Added comprehensive simulation tests demonstrating parameter
    recovery (c, ar1, sigma, gamma).
