tulpa_gaussian: Fit a Gaussian linear model via tulpa's generic engine

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Fit a Gaussian linear model via tulpa's generic engine

Description

Proof-of-concept function demonstrating the tulpa generic interface: the Gaussian likelihood is supplied as a LikelihoodSpec and fitted by the production NUTS sampler every other ModelData backend runs (dual-averaged step size, adapted diagonal mass matrix, reverse-mode AD gradient checked against finite differences before sampling). Fits y ~ Normal(X * beta, sigma).

Usage

tulpa_gaussian(
  formula,
  data,
  beta_prior = .tulpa_default_beta_prior("gaussian"),
  control = list()
)

Arguments

formula	A formula (e.g., y ~ x1 + x2)
data	A data frame
beta_prior	Fixed-effect prior as list(mean, sd): a mean-zero (mean = 0) Gaussian on every coefficient with SD sd (default the engine default, prior_normal(0, 2.5)).
control	List of sampler knobs: iter (total iterations, default 2000), warmup (default 1000), max_treedepth (default 10), adapt_delta (target acceptance statistic, default 0.8), seed (NULL draws from the session RNG).

Value

A tulpa_fit list with draws (post-warmup draws of beta[j] and log_sigma), means, sigma (exp() of the posterior mean of log_sigma), accept_rate (the mean NUTS acceptance statistic over the post-warmup iterations), the per-iteration accept_prob, divergent, treedepth and log_prob, the adapted step size epsilon, and formula, N, p.

Examples

set.seed(1)
d <- data.frame(x = rnorm(40))
d$y <- 1 + 2 * d$x + rnorm(40, 0, 0.6)
fit <- tulpa_gaussian(y ~ x, d, control = list(iter = 1000, warmup = 500,
                                              seed = 1))
fit$means
fit$accept_rate

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.