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Fit a Gaussian linear model via tulpa's generic engine

Source: R/fit_gaussian.R
tulpa_gaussian.Rd

Proof-of-concept function demonstrating the tulpa generic interface. Fits y ~ Normal(X * beta, sigma) with HMC sampling.

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 numerical / sampler knobs: iter (total iterations, default 2000), warmup (default 1000), step_size (HMC step size, default 0.05), n_leapfrog (default 10), seed (NULL draws from the session RNG).

Value

A list with draws matrix, posterior means, and metadata