NEWS.md
In DynCount: Bayesian Dynamic Models for Count Time Series

DynCount 0.2.0

New observation family family = "multinomial" for choice counts: y is an n x K matrix, the row totals are the known trials, and each non-baseline category has its own latent additive-log-ratio (ALR) series log(p_k / p_baseline) following the chosen latent dynamics and innovation structure, with no parameters shared across categories. The baseline is selected with the new baseline argument (default "largest"). Per-category ALR offsets, forecasting (forecast_trials = future totals, by default the last non-zero row total), summary(), predict() (including type = "prob"), forecast() and the plot functions (one panel per category, category argument) all support the new family; posterior draws carry a trailing category dimension. With K = 2 the model coincides with the binomial family.
New simulator simulate_dynamic_multinomial(). Its baseline can be given as a column index or a category name, and its offset as a scalar, a per-category vector or a matrix.
Forecasting is now post hoc: forecast(fit, horizon = H) forward-simulates the latent path from every stored posterior draw (with increments from the fitted innovation structure) and draws responses from the observation model. This targets the same posterior predictive distribution as the previous in-sampler forecasts, but the horizon, the forecast offset and the forecast trials can now be chosen after fitting. forecast() and plot_forecast() gain horizon, forecast_offset, forecast_trials and seed arguments. Fitting with horizon = H still stores an H-step forecast in the fit.
The draws of the stochastic-volatility parameters (sv_mu, sv_phi, sv_sigma) are now stored and reported by summary(). The draws of the mixture weights and component variances (mix_weight, mix_var) and of the overall innovation scale (scale) are stored as well.
The example data sets gain a date column (the Monday of each ISO week).

The default prior on the innovation variance is now the vague InvGamma(0.01, 0.01) instead of InvGamma(2.5, 0.5), which put almost no mass below sigma = 0.1 and could force rough latent paths on smooth series. Pass dynamic_prior(var_shape = 2.5, var_rate = 0.5) to recover the old behaviour. See ?dynamic_prior for the trade-offs.
The relative variances of the "mixture" components now have their own hyperparameters mix_var_shape / mix_var_rate (defaults 2.5 / 0.5, i.e. unchanged) rather than reusing var_shape / var_rate.

draws$z and draws$sig2 now have one column per observation. The initial latent state is stored separately as draws$z0, and forecast states are only in draws$forecast_z.
draws$gate and draws$pi_open are NULL for fits without zero inflation, and draws$nu is NULL unless innovations = "t".
forecast() is now the generic from the generics package (re-exported), so forecast(fit) also works when the forecast or fable packages are attached.
zero_inflation in fit_dynamic_model() must be a single TRUE/FALSE; other values (e.g. a probability, as in the simulators) are an error, as is combining zero_inflation = TRUE with a different explicit zeros.

The sampler works on a matrix of latent series; the Poisson and binomial families are the single-column case.
The latent states are updated in a checkerboard order. As the GMRF precision is tridiagonal, the states with odd and with even time index are conditionally independent given each other, so each half is updated in one vectorised step with the same single-site adaptive Metropolis moves. This makes fitting roughly 7 to 17 times faster.
The Student-t degrees of freedom are updated with the mixing weights integrated out (a partially collapsed Gibbs step), which improves their effective sample size several-fold.

A seed argument no longer changes the global random number stream. The previous RNG state is restored after fitting, forecasting and simulating.
Arguments passed through ... to the plot functions (e.g. main, xlab, ylim) now override the defaults instead of causing an error.
Warnings for arguments that are ignored (trials or forecast_trials for the Poisson family, baseline for non-multinomial families, forecast inputs with horizon = 0) and for forecasting a model with an offset without a forecast_offset.
simulate_dynamic_binomial() validates trials.