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ModalForecast 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.

ModalForecast 0.1.0

• 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).

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.