sdmTMB 1.1.0

New features

• sdmTMB() gains experimental support for CAR/SAR areal models as an
  alternative to the SPDE. See the relevant vignette at
  https://sdmtmb.github.io/sdmTMB/articles/index.html

• sdmTMB() gains non-local covariate support, including spatial diffusion and
  time lags, with new nonlocal_formula and nonlocal_data arguments.
  plot_nonlocal_kernel() and plot_nonlocal_covariate() support visualizing
  the resulting kernels and covariates (#507). See the relevant vignette at
  https://sdmtmb.github.io/sdmTMB/articles/index.html

• sdmTMB() now supports uncorrelated random slopes and intercepts with the
  form (1 + slope_var || group), and reports the results correctly.
  (@Joseph-Barss, #536).

• predict.sdmTMB() gains allow_new_levels for prediction with new random
  intercept or slope levels; new levels use the population value (#196, #480).

• get_index() now reports index totals on a log-total scale after integrating
  response-scale predictions. This preserves existing behaviour for log-link
  abundance models and allows binomial and beta-binomial proportion models,
  including logit and cloglog links, to use area as an explicit
  standardization multiplier for expected-count indices.

• get_index() gains derived_link to override the inverse link, which is
  useful when fitting Bernoulli data with a cloglog link and predicting an
  expected count from a thinned count process (#339).

• sdmTMB() gains experimental Restricted Spatial Regression (RSR) fixed
  effects adjusted for spatial confounding with the random fields (Hanks et al.
  2015; Diaz and Thorson 2025). Use
  control = sdmTMBcontrol(get_rsr = TRUE) and
  tidy(fit, effects = "rsr").

Minor improvements and fixes

• Fix compatibility with next RcppEigen (#538).

• print() and tidy() now display the correctly transformed correlation
  between a random slope and intercept.

• sdmTMB() now uses tryCatch() around Newton loops in optimization so those
  steps abort cleanly instead of producing an error.

• predict.sdmTMB() now allows missing factor levels when predicting with
  time-varying factor predictors (#533).

• sanity() now identifies the fixed effects, providing more useful
  diagnostics (#506).

• delta_poisson_link_gamma() and delta_poisson_link_lognormal() now raise
  deprecation errors instead of warnings. Use
  delta_gamma(type = "poisson-link") and
  delta_lognormal(type = "poisson-link") instead.

• spatial_varying now respects its own model matrix. The (Intercept) column
  is only dropped when spatial = "on", in which case the ordinary spatial
  field omega_s is used as the SVC intercept or reference-level field.

  A single informational message is issued at fit time, suppressible via
  silent = TRUE.

  The only material change in fitted-model structure versus published releases
  is for spatial = "off", spatial_varying = ~ 1 + factor: this previously
  stripped the intercept and zeroed omega_s, leaving only K - 1 deviation
  fields with no reference field. It now fits a genuine SVC intercept field
  plus K - 1 deviation fields. A warning is
  emitted for this specification for now.

  The previous message suggesting spatial = "off" when using
  spatial = "on", spatial_varying = ~ 0 + factor_var has been removed; this
  is a valid model specification (global spatial field plus per-level SVC
  deviations) although it can be more challenging to estimate.

• get_index() now returns the correct se_natural column
  (@CataRoman, @gavinfay, #523).

• residuals() now uses the correct inverse link for encounter and
  positive-component mechanisms, including offsets, in Poisson-link delta
  models, avoiding spurious NaN and Inf values in the first linear
  predictor residuals. For most scenarios, simulation-based residuals from
  dharma_residuals() remain the most straightforward choice for combined
  residuals in delta models (#512).

• predict.sdmTMB() now works when newdata contains NA in the response
  column for models with random effects and the response column is present but
  unused for prediction (#508).