Package index • tulpa

Front door

The main fitter, its formula surface, and the mode / family helpers.

tulpa(): Fit a tulpa model
tulpa_parse_formula(): Parse a mixed-model formula
findbars(): Find all bar terms in a formula's parse tree
nobars(): Remove all bar terms from a formula's parse tree
inference_mode_info(): Print inference mode information
validate_mode(): Validate that a fit used the expected mode
tulpa_family(): Construct a minimal tulpa_family for simulation
tulpa_gaussian(): Fit a Gaussian linear model via tulpa's generic engine
tulpa_build_model_data(): Build model matrices from a parsed formula
tulpa_simulate(): Simulate data from a tulpa model

Tier 2 – structured approximations

Laplace, nested Laplace, and the free-Sigma random-effect backends.

tulpa_laplace(): Fit a model via Laplace approximation
tulpa_laplace_beta(): Fit a beta-regression model via Laplace, estimating the precision
tulpa_nested_laplace(): Nested Laplace approximation for latent Gaussian models
tulpa_nested_laplace_joint(): Joint multi-likelihood nested Laplace approximation
fit_st_nested(): Fit an additive spatiotemporal GLM by nested Laplace
tulpa_hyper_grid(): Outer hyperparameter-grid integration with a user-supplied inner fit
tulpa_re_cov_nested(): Nested-Laplace integration over random-effect covariances
tulpa_re_aghq(): Adaptive Gauss-Hermite refinement of a grouped random-effect covariance
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
agq_fit(): Adaptive Gauss-Hermite quadrature for one-RE GLMMs
fit_spde(): Fit a Spatial Model using SPDE Laplace Approximation
tulpa_ep(): Expectation-Propagation fit for a GLM
tulpa_multinomial(): Multinomial (nominal K-class) logistic regression via Laplace
tulpa_ordinal(): Ordinal (ordered K-class) cumulative-logit regression via Laplace
imh_laplace(): Independence Metropolis-Hastings with Laplace proposal

Tier 1 – exact MCMC and debias

tulpa_gibbs(): Fit via Polya-Gamma Gibbs sampler
tulpa_re_cov_gibbs(): Gibbs estimation of random-effect covariances (exact-target debias)
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_tgmrf(): Fit a custom tgmrf latent block
tgmrf(): User-defined GMRF latent block
tgmrf_cpp(): User-defined GMRF latent block, compiled C++ backend
tulpa_cache_dir(): Default cache directory for tgmrf_cpp()-compiled DLLs
tulpa_cache_clear(): Remove compiled blocks from the tgmrf_cpp() cache
mala(): Metropolis-Adjusted Langevin Algorithm (MALA)
tulpa_integrator(): Select the symplectic integrator for HMC and NUTS
with_tulpa_integrator(): Run an expression under a chosen symplectic integrator

Other approximations

pathfinder(): Pathfinder: variational warm-start via L-BFGS + ELBO scoring
bridge_sampling(): Bridge sampling for marginal likelihood

Latent structure

Spatial, temporal, and varying-coefficient constructors.

spatial(): Areal spatially varying coefficient field
spatial_car(): CAR / ICAR spatial structure
spatial_car_proper(): Proper CAR spatial structure
spatial_bym2(): BYM2 spatial structure
spatial_gp(): Gaussian process spatial structure (NNGP)
spatial_multiscale(): Multi-Scale Gaussian Process spatial structure
spatial_svc(): Spatially varying coefficient structure
spatial_rsr(): Restricted Spatial Regression (RSR)
spatial_spde(): SPDE Spatial Field (Matern via Triangular Mesh)
spatial_spde_custom(): SPDE Spatial Field from Custom Matrices
temporal_rw1(): RW1 temporal structure (First-order Random Walk)
temporal_rw2(): RW2 temporal structure (Second-order Random Walk)
temporal_ar1(): AR1 temporal structure (First-order Autoregressive)
temporal_ar2(): AR(2) temporal latent field (second-order autoregressive)
temporal_ar(): AR(p) temporal latent field (general-order autoregressive)
temporal_gp(): Gaussian Process temporal structure
temporal_multiscale(): Multi-scale temporal structure
temporal_tvc(): Time-varying coefficient structure
temporal_rtr(): Restricted temporal regression (RTR)
spatiotemporal(): Spatiotemporal interaction specifications for tulpa
spatiotemporal_gp(): Non-separable spatiotemporal GP
svc(): Extract spatially-varying coefficients from a fitted model
tvc(): Extract temporally-varying coefficients from a fitted model
latent(): Mark an expression as a latent block in a tulpa formula
latent_factor(): Create a latent factor specification
adjacency(): Construct a spatial adjacency graph for areal models
check_adjacency(): Validate a spatial adjacency matrix
node_index(): Map cell identifiers to graph node indices
compute_nngp_neighbors(): Compute nearest neighbors for NNGP
tulpa_bar_field_specs(): Expand a varying-coefficient bar into per-column field specs
tulpa_bar_field_replicate(): Replicate an areal graph across the levels of a factor (replicated CAR)
tulpa_is_spatial_bar(): Recognize an inline varying-coefficient bar

Priors

The prior surface and the individual prior builders.

tulpa_priors(): Prior specification for tulpa models
priors_default(): Show default priors for a tulpa family
prior_from_spec(): Build a prior list for tulpa_nested_laplace() from a tulpa spec object
prior_normal(): Normal prior
prior_beta(): Beta prior
prior_gamma(): Gamma prior
prior_exponential(): Exponential prior
prior_half_cauchy(): Half-Cauchy prior
prior_half_normal(): Half-normal prior
prior_pc(): Penalized complexity (PC) prior
re_cov_pc_lkj_prior(): PC + LKJ hyperprior for a random-effect covariance

Methods, criteria, and prediction

tulpa_criteria(): Model criteria from a pointwise log-likelihood
dic() cpo(): DIC and CPO
tulpa_kfold(): K-fold cross-validation for a tulpa fit
tulpa_reloo(): Selective refit of high-Pareto-k observations (reloo)
tulpa_psis(): Pareto-smoothed importance sampling
tulpa_loglik(): Streaming pointwise log-likelihood
bayes_R2(): Bayesian R-squared
tulpa_powerscale_sensitivity(): Power-scaling prior / likelihood sensitivity
compare_models(): Compare models by information criteria
model_average(): Model-averaged predictions
posterior_predict(): Posterior predictive replicates
pp_check(): Posterior predictive check
prior_predict(): Prior predictive simulation
reexports tidy glance: Objects exported from other packages

Reading a fit

Accessors for a fitted model: the coefficient surface, the posterior draws in whatever representation the backend produced, and the extracted latent effects.

fixef(): Fixed-effect coefficients (lme4-compatible)
ranef(): Random-effect summaries
VarCorr(): Random-effect variances and correlations
posterior_sample(): Posterior parameter sample from a fit
mcmc_draws(): MCMC chain draws from a fit
tulpa_posterior_draws(): Posterior draws from a nested-Laplace fit
tulpa_draws_array(): Posterior draws as a 3D array
as_draws() as_draws_array() as_draws_matrix() as_draws_df() as_draws_rvars(): Posterior draws in the posterior package's format
temporal(): Extract temporal effects from a fitted model
spatiotemporal_effects(): Extract spatiotemporal effects from fitted model
smooth_effects(): Extract fitted covariate smooths
latent_factors(): Extract latent factor posteriors from fit

Diagnostics

Convergence for sampled fits, approximation reliability for deterministic ones, and the residual / goodness-of-fit surface both share.

diagnostics(): Posterior diagnostics for a fitted model
mcmc_diagnostics() deprecated: MCMC convergence diagnostics
laplace_diagnostics() deprecated: Approximation-reliability diagnostics for a deterministic nested-Laplace fit
check_diagnostics(): Quick convergence check
diagnostic_summary(): Comprehensive Diagnostic Summary
select_main_params(): Select the "main" model parameters for diagnostic display
n_divergent(): Number of divergent transitions
geweke_test(): Geweke Convergence Test
moran_i(): Moran's I test for spatial autocorrelation in residuals
durbin_watson(): Durbin-Watson test for temporal autocorrelation
tulpa_variogram(): Empirical semivariogram of residuals
pit_residuals(): PIT (Probability Integral Transform) residuals
tulpa_pit(): Probability integral transform from a predictive CDF
test_uniformity(): Test uniformity of PIT residuals
test_dispersion(): Test for over- or underdispersion
test_outliers(): Test for outliers (simulation envelope)
test_zero_inflation(): Test for zero inflation
check_model(): Diagnostic panel plot
spatial_range(): Extract spatial range and variance from a fitted spatial model
temporal_corr(): Extract temporal correlation parameters from a fitted model
post_hoc_lm(): Fit a post-hoc linear model on estimated parameters

Calibration

Simulation-based calibration. Where the diagnostics above score one fit, these score whether an inference algorithm’s posteriors are calibrated across the generative model, by reading the whole marginal CDF.

sbc() summary(<sbc>) plot(<sbc>): Simulation-based calibration
sbc_mixture() sbc_normal() sbc_discrete() sbc_rank() sbc_draws(): Predictive shapes an SBC fitter reports

Plots

plot_rhat(): Plot Rhat Convergence Diagnostic
plot_ess(): Plot Effective Sample Size Diagnostic
plot_acf(): Plot Autocorrelation Functions
plot_energy(): Plot Energy Diagnostic (E-BFMI)
plot_divergences(): Plot Divergent Transitions
plot_pairs(): Plot Bivariate Parameter Posteriors (Pairs Plot)
plot_diagnostics(): Diagnostic Plotting Functions for tulpa Models
plot_map(): Plot spatial predictions as a map
plot_map_panel(): Plot multiple maps in a grid

Outer-grid integration and utilities

The node designs the outer hyperparameter integration is laid on, the axis-provenance surface, and the small numerical helpers.

ccd_grid(): Central Composite Design (CCD) grid for nested-Laplace integration
ccd_weights(): Corrected R-INLA CCD integration weights
ccd_to_theta(): Map standardised CCD coordinates to physical hyperparameters
hyper_axis_spec(): Describe one outer-grid hyperparameter axis
auto_grid(): Mark an outer-grid setting as a default rather than a pin
is_auto_grid(): Is an outer-grid setting marked as a default?
rubins_pool(): Pool multiple imputation draws via Rubin's rules
sn_cdf(): Skew-normal CDF
sn_quantile(): Skew-normal quantile
sn_match(): Match three cumulants to a skew-normal parameterisation
tulpa_profile(): Profile the inner Laplace solve by phase
tulpa_check_control(): Validate a control = list() surface against its canonical key set