Two-stage multiple-imputation nowcast

Usage

nowcast_twostage(
  model,
  m,
  X = NULL,
  d_star = NULL,
  max_time = NULL,
  target = NULL,
  delay_window = 120L,
  K = 25L,
  floor_mu = 0.08,
  floor_sig_frac = 0.08,
  np_spread = 1,
  n_draws_per = 200L,
  phi = lognormal_prior(log(20), 0.5),
  probs = c(0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.975),
  seed = sample.int(.Machine$integer.max, 1)
)

Arguments

model: A model() object (LogNormal delay).
m: Observation matrix [event_time, count, delay, strata...].
X: Optional covariate matrix (max_time rows).
d_star: Optional max-observable-delay vector.
max_time: Time-window length; defaults to max(m[, 1]).
target: Event-time to nowcast (default newest).
delay_window: Recent window length for the Stage-1 delay fit.
K: Number of delay imputations.
floor_mu: Floor on the log-mean imputation SD (parametric families).
floor_sig_frac: Floor on the delay-SD imputation SD (fraction of sigma).
np_spread: Dirichlet only: covariance-inflation factor for the simplex imputation (samples delay_logits from the Stage-1 Laplace posterior with covariance scaled by np_spread). Default 1 (the raw, well-informed full-series posterior); values > 1 widen the simplex spread.
n_draws_per: Posterior nowcast draws per imputation.
phi: NB overdispersion prior (default lognormal_prior(log(20), 0.5)).
probs: Quantile probabilities to report.
seed: Optional RNG seed.

Value

A list with quantiles, median, pooled draws, the rung used ("multi", "anchored", or "onestage"), and n_samp (imputations pooled).