NEWS
DynCount 0.2.0
New features
- 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).
Changes to defaults
- 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.
Breaking changes
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.
Sampler
- 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.
Minor improvements and fixes
- 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.
DynCount 0.1.0 (2026-07-14)