Build a GLMM log-posterior (and gradient) from a model-data bundle.

Usage

build_glmm_logpost(
  bundle,
  family,
  sigma_re = NULL,
  n_trials = NULL,
  phi = 1,
  beta_prior = .tulpa_default_beta_prior("glmm_logpost"),
  weights = NULL,
  phi2 = NULL
)

Arguments

bundle: Output of tulpa_build_model_data() (needs y, X, offset, re_terms, n_obs, n_fixed).
family: Character family name (see family_names()).
sigma_re: Numeric vector of random-effect SDs, one per RE term. Length must equal length(bundle$re_terms). Ignored when there are no RE terms.
n_trials: Binomial denominators (or NULL).
phi: Dispersion passed to the family, held fixed. One convention at every door: for gaussian / lognormal this is the residual VARIANCE (the SD is sqrt(phi)), for neg_binomial_2 the size, gamma the shape, beta the precision, t the scale; binomial and poisson ignore it. The compiled kernels parameterize the two variance families by the residual SD and are handed sqrt(phi) at the boundary.
beta_prior: list(mean, sd) Gaussian prior on the fixed effects (scalars, recycled). Defaults to the engine default, prior_normal(0, 2.5).
weights: Optional per-observation likelihood weights (length n_obs): each row's log-likelihood and score contribution is scaled by its weight.
phi2: Optional second dispersion (Student-t degrees of freedom).

Value

A list with:

log_posterior(theta) – scalar log-posterior (up to a constant).
grad_log_posterior(theta) – gradient vector.
dim – length of theta.
init – a zero starting vector of length dim.
unpack(theta) – list(beta, u = list of n_groups x n_coefs matrices).
param_names – character labels aligned with theta.