tulpaObs 0.0.196

0.0.196 (2026-08-11)

• The information criteria score a random effect the fit carried
  (#215). On the grid-integrated (nested_laplace) route,
  occu_cover() scored every observation-arm random effect at the population
  mean in WAIC, LOO, CPO, PPC and PIT, and an occupancy-arm one likewise. Both
  now reach the criteria through the same per-visit and per-site offset path the
  sampled route uses. This moves existing numbers: elpd_waic -17.939 ->
  -6.406 on a detection-arm RE at sigma_re_p 1.10 over 216 visits, -125.94 ->
  -64.41 on a cover-arm RE over 400 visits, and -209.525 -> -200.180 on an
  occupancy-arm RE at sigma_re 1.57. A fit carrying no random effect is
  identical to the bit. The occupancy term also carries its grouping variable
  and factor levels now, so ranef() labels it like the observation arms and
  predict(newdata = ) matches on it.

• A bare areal term on occu_cover() loads on occurrence only under nuts,
  as it already did under nested_laplace (#217). The same
  input previously fit two different models: the deterministic backend decoupled
  the cover arm while the sampler estimated a copy amplitude off the default
  axis (mean 0.78 on a 36-cell fixture). A term's process is the formula it sits
  in, and copy() is what couples it to the other arm. This changes fitted
  values for existing no-copy spatial NUTS fits. Declaring copy(spatial())
  reproduces the previous numbers to every printed digit on all three coupled
  recovery fixtures.

• occu_cover() samples a second (spatially-varying-coefficient) areal field
  under nuts (#214). The sampler carried one field block, so a
  trend surface was confined to the grid-integrated path. Each block now carries
  its own basis, site-to-node map, per-site design weight and its own sampled
  (sigma, rho, alpha). Reported as fit$trend_field / trend_fields, with
  per-block suffixed hyperparameters in hyper_draws. Both surfaces recover with
  zero divergences.

• A per-source detection formula can carry a structured term
  (#216). An integrated detection-arm spde() field was wired in
  the fitter and unreachable from tobs(). Per-source field correlation with
  truth 0.87-0.93; the fields are reported in fit$spatial_field_det. Gating
  this uncovered a silent misplacement: under method = "nested_laplace" a
  detection-arm term was fit against the occupancy arm, on the single-season path
  as well as the integrated one. Both now error with a pointer.

• Simulation-based calibration runs on eight more families
  (#207): occu, count, abun, royle_nichols, occu_ttd,
  fp_occu, removal and distance, each verified end to end against a
  deliberately mis-scaled control. A structured term and a visit-level
  observation design are refused rather than approximated.

• The SBC replicate field is drawn at the width the joint engine fits
  (#213). The generator normalised its ICAR draw to geo-mean
  marginal SD 1 while the joint nested-Laplace engine carries the amplitude
  against the raw Q = D - W, so both reported field SDs were off by one common
  factor while their ratio stayed clean. Ranks return inside the band. Note that
  the joint path reports sigma as a raw amplitude while the sampled path
  reports a geo-mean marginal SD; compare a fit against a simulator on the field,
  not on the reported scalar.