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Normalize an optional fixed-effect Gaussian prior

Source: R/fit_laplace.R
dot-normalize_beta_prior.Rd

Validates beta_prior and recycles scalar mean / sd to length p. Returns NULL (use the built-in weak prior) or list(mean, sd) with both vectors of length p. Shared by tulpa_laplace() and the EM driver so the validation rules live in one place.

Usage

.normalize_beta_prior(beta_prior, p)

Arguments

beta_prior

NULL, or a list with sd (required) and optional mean.

p

Number of fixed effects (ncol(X)).