VarCorr                 Random-effect variances and correlations
adjacency               Construct a spatial adjacency graph for areal
                        models
agq_fit                 Adaptive Gauss-Hermite quadrature for one-RE
                        GLMMs
as_draws                Posterior draws in the posterior package's
                        format
auto_grid               Mark an outer-grid setting as a default rather
                        than a pin
bayes_R2                Bayesian R-squared
bridge_sampling         Bridge sampling for marginal likelihood
check_adjacency         Validate a spatial adjacency matrix
check_diagnostics       Quick convergence check
check_model             Diagnostic panel plot
coef.tulpa_fit          Fixed-effect coefficients
compare_models          Compare models by information criteria
confint.tulpa_fit       Credible intervals for the fixed effects
criteria_doors          DIC and CPO
diagnostic_summary      Comprehensive Diagnostic Summary
diagnostics             Posterior diagnostics for a fitted model
durbin_watson           Durbin-Watson test for temporal autocorrelation
fit_spde                Fit a Spatial Model using SPDE Laplace
                        Approximation
fit_st_nested           Fit an additive spatiotemporal GLM by nested
                        Laplace
fitted.tulpa_fit        Fitted values (population level)
fixef                   Fixed-effect coefficients (lme4-compatible)
geweke_test             Geweke Convergence Test
glance.tulpa_fit        Model-level summary statistics
                        (broom-compatible)
imh_laplace             Independence Metropolis-Hastings with Laplace
                        proposal
inference_mode_info     Print inference mode information
is_auto_grid            Is an outer-grid setting marked as a default?
laplace_diagnostics     Approximation-reliability diagnostics for a
                        deterministic nested-Laplace fit
latent                  Mark an expression as a latent block in a tulpa
                        formula
latent_factor           Create a latent factor specification
latent_factors          Extract latent factor posteriors from fit
logLik.tulpa_fit        Log-likelihood at the posterior mean
mala                    Metropolis-Adjusted Langevin Algorithm (MALA)
mcmc_draws              MCMC chain draws from a fit
model_average           Model-averaged predictions
moran_i                 Moran's I test for spatial autocorrelation in
                        residuals
n_divergent             Number of divergent transitions
nobs.tulpa_fit          Number of observations in a tulpa fit
node_index              Map cell identifiers to graph node indices
pathfinder              Pathfinder: variational warm-start via L-BFGS +
                        ELBO scoring
pit_residuals           PIT (Probability Integral Transform) residuals
plot.tulpa_fit          Plot fixed-effect posteriors
plot.tulpa_prior_predict
                        Plot method for tulpa_prior_predict
plot.tulpa_st_summary   Plot method for spatiotemporal effects
plot.tulpa_svc_posterior
                        Plot method for tulpa_svc_posterior
plot.tulpa_temporal_posterior
                        Plot method for tulpa_temporal_posterior
plot.tulpa_tvc_posterior
                        Plot method for tulpa_tvc_posterior
plot_acf                Plot Autocorrelation Functions
plot_divergences        Plot Divergent Transitions
plot_energy             Plot Energy Diagnostic (E-BFMI)
plot_ess                Plot Effective Sample Size Diagnostic
plot_map                Plot spatial predictions as a map
plot_map_panel          Plot multiple maps in a grid
plot_pairs              Plot Bivariate Parameter Posteriors (Pairs
                        Plot)
plot_rhat               Plot Rhat Convergence Diagnostic
post_hoc_lm             Fit a post-hoc linear model on estimated
                        parameters
posterior_predict       Posterior predictive replicates
posterior_sample        Posterior parameter sample from a fit
pp_check                Posterior predictive check
predict.tulpa_fit       Predict at new covariate values (population
                        level)
print.tulpa_diagnostic_summary
                        Print method for diagnostic summary
print.tulpa_geweke      Print method for Geweke test
print.tulpa_gp          Print method for tulpa_gp
print.tulpa_hsgp        Print method for tulpa_hsgp
print.tulpa_latent      Print method for tulpa_latent
print.tulpa_multiscale
                        Print method for tulpa_multiscale
print.tulpa_nested_laplace
                        Print method for nested-Laplace fits
print.tulpa_prior       Print method for tulpa_prior
print.tulpa_prior_predict
                        Print method for tulpa_prior_predict
print.tulpa_priors      Print method for tulpa_priors
print.tulpa_rsr         Print method for tulpa_rsr
print.tulpa_simulate    Print method for tulpa_simulate
print.tulpa_spatial     Print method for tulpa_spatial
print.tulpa_svc         Print method for tulpa_svc
print.tulpa_svc_posterior
                        Print method for tulpa_svc_posterior
print.tulpa_temporal    Print method for tulpa_temporal
print.tulpa_temporal_gp
                        Print method for tulpa_temporal_gp
print.tulpa_temporal_multiscale
                        Print method for tulpa_temporal_multiscale
print.tulpa_temporal_posterior
                        Print method for tulpa_temporal_posterior
print.tulpa_tvc         Print method for tulpa_tvc
print.tulpa_tvc_posterior
                        Print method for tulpa_tvc_posterior
prior_beta              Beta prior
prior_exponential       Exponential prior
prior_from_spec         Build a 'prior' list for
                        'tulpa_nested_laplace()' from a tulpa spec
                        object
prior_gamma             Gamma prior
prior_half_cauchy       Half-Cauchy prior
prior_half_normal       Half-normal prior
prior_normal            Normal prior
prior_pc                Penalized complexity (PC) prior
prior_predict           Prior predictive simulation
priors_default          Show default priors for a tulpa family
ranef                   Random-effect summaries
re_cov_pc_lkj_prior     PC + LKJ hyperprior for a random-effect
                        covariance
residuals.tulpa_fit     Residuals from a tulpa fit
rubins_pool             Pool multiple imputation draws via Rubin's
                        rules
sbc                     Simulation-based calibration
sbc_predictive          Predictive shapes an SBC fitter reports
select_main_params      Select the "main" model parameters for
                        diagnostic display
simulate.tulpa_fit      Simulate responses from a fitted tulpa model
smooth_effects          Extract fitted covariate smooths
spatial                 Areal spatially varying coefficient field
spatial_bym2            BYM2 spatial structure
spatial_car             CAR / ICAR spatial structure
spatial_car_proper      Proper CAR spatial structure
spatial_gp              Gaussian process spatial structure (NNGP)
spatial_multiscale      Multi-Scale Gaussian Process spatial structure
spatial_range           Extract spatial range and variance from a
                        fitted spatial model
spatial_rsr             Restricted Spatial Regression (RSR)
spatial_spde            SPDE Spatial Field (Matern via Triangular Mesh)
spatial_spde_custom     SPDE Spatial Field from Custom Matrices
spatial_svc             Spatially varying coefficient structure
spatiotemporal          Spatiotemporal interaction specifications for
                        tulpa
spatiotemporal_effects
                        Extract spatiotemporal effects from fitted
                        model
spatiotemporal_gp       Non-separable spatiotemporal GP
summary.tulpa_fit       Posterior summary of the fixed effects
summary.tulpa_svc_posterior
                        Summary method for tulpa_svc_posterior
summary.tulpa_temporal_posterior
                        Summary method for tulpa_temporal_posterior
summary.tulpa_tvc_posterior
                        Summary method for tulpa_tvc_posterior
svc                     Extract spatially-varying coefficients from a
                        fitted model
temporal                Extract temporal effects from a fitted model
temporal_ar             AR(p) temporal latent field (general-order
                        autoregressive)
temporal_ar1            AR1 temporal structure (First-order
                        Autoregressive)
temporal_ar2            AR(2) temporal latent field (second-order
                        autoregressive)
temporal_corr           Extract temporal correlation parameters from a
                        fitted model
temporal_gp             Gaussian Process temporal structure
temporal_multiscale     Multi-scale temporal structure
temporal_rtr            Restricted temporal regression (RTR)
temporal_rw1            RW1 temporal structure (First-order Random
                        Walk)
temporal_rw2            RW2 temporal structure (Second-order Random
                        Walk)
temporal_tvc            Time-varying coefficient structure
test_dispersion         Test for over- or underdispersion
test_outliers           Test for outliers (simulation envelope)
test_uniformity         Test uniformity of PIT residuals
test_zero_inflation     Test for zero inflation
tgmrf                   User-defined GMRF latent block
tgmrf_cpp               User-defined GMRF latent block, compiled C++
                        backend
tidy.tulpa_fit          Tidy fixed-effect table (broom-compatible)
tulpa                   Fit a tulpa model
tulpa_bar_field_replicate
                        Replicate an areal graph across the levels of a
                        factor (replicated CAR)
tulpa_bar_field_specs   Expand a varying-coefficient bar into
                        per-column field specs
tulpa_cache_clear       Remove compiled blocks from the tgmrf_cpp()
                        cache
tulpa_cache_dir         Default cache directory for
                        'tgmrf_cpp()'-compiled DLLs
tulpa_check_control     Validate a 'control = list()' surface against
                        its canonical key set
tulpa_criteria          Model criteria from a pointwise log-likelihood
tulpa_diagnostics       Simulation-Based Diagnostics for tulpa Models
tulpa_draws_array       Posterior draws as a 3D array
tulpa_eb                Empirical-Bayes random-effect covariances
tulpa_em_laplace        Fit a latent-variable model via EM + Laplace
                        approximation
tulpa_em_mc             Generic Monte-Carlo EM driver
tulpa_ep                Expectation-Propagation fit for a GLM
tulpa_family            Construct a minimal tulpa_family for simulation
tulpa_formula           Formula parsing for tulpa models
tulpa_gaussian          Fit a Gaussian linear model via tulpa's generic
                        engine
tulpa_gibbs             Fit via Polya-Gamma Gibbs sampler
tulpa_hyper_grid        Outer hyperparameter-grid integration with a
                        user-supplied inner fit
tulpa_integrator        Select the symplectic integrator for HMC and
                        NUTS
tulpa_is_spatial_bar    Recognize an inline varying-coefficient bar
tulpa_kfold             K-fold cross-validation for a tulpa fit
tulpa_laplace           Fit a model via Laplace approximation
tulpa_laplace_beta      Fit a beta-regression model via Laplace,
                        estimating the precision
tulpa_latent            Latent Factor Specification for Unmeasured
                        Confounders
tulpa_loglik            Streaming pointwise log-likelihood
tulpa_multinomial       Multinomial (nominal K-class) logistic
                        regression via Laplace
tulpa_nested_laplace    Nested Laplace approximation for latent
                        Gaussian models
tulpa_nested_laplace_joint
                        Joint multi-likelihood nested Laplace
                        approximation
tulpa_nuts_beta         Fit a beta-regression model via NUTS (joint
                        sampling of beta + log_phi)
tulpa_nuts_spde         Sample an SPDE GLM via NUTS, optionally jointly
                        over Matern hypers
tulpa_ordinal           Ordinal (ordered K-class) cumulative-logit
                        regression via Laplace
tulpa_parse_formula     Parse a mixed-model formula
tulpa_pit               Probability integral transform from a
                        predictive CDF
tulpa_posterior_draws   Posterior draws from a nested-Laplace fit
tulpa_posterior_draws.tulpa_nested_laplace_joint
                        Posterior draws from a joint nested-Laplace fit
tulpa_powerscale_sensitivity
                        Power-scaling prior / likelihood sensitivity
tulpa_priors            Prior specification for tulpa models
tulpa_profile           Profile the inner Laplace solve by phase
tulpa_psis              Pareto-smoothed importance sampling
tulpa_re_aghq           Adaptive Gauss-Hermite refinement of a grouped
                        random-effect covariance
tulpa_re_cov_gibbs      Gibbs estimation of random-effect covariances
                        (exact-target debias)
tulpa_re_cov_nested     Nested-Laplace integration over random-effect
                        covariances
tulpa_reloo             Selective refit of high-Pareto-k observations
                        (reloo)
tulpa_simulate          Simulate data from a tulpa model
tulpa_spatial           Spatial structure specifications for tulpa
tulpa_temporal          Temporal structure specifications for tulpa
tulpa_tgmrf             Fit a custom tgmrf latent block
tulpa_validate          Posterior predictive checks for tulpa models
tulpa_variogram         Empirical semivariogram of residuals
tvc                     Extract temporally-varying coefficients from a
                        fitted model
validate_mode           Validate that a fit used the expected mode
vcov.tulpa_fit          Variance-covariance matrix of the fixed effects
with_tulpa_integrator   Run an expression under a chosen symplectic
                        integrator
