gibbs_cs_wish: Cross-sectional FoSR using a Gibbs sampler and Wishart prior
In jeff-goldsmith/BayesFoSR: Bayesian Function-on-Scalar Regression

Description Usage Arguments Author(s) References

Fitting function for function-on-scalar regression for cross-sectional data. This function estimates model parameters using a Gibbs sampler and estimates the residual covariance surface using a Wishart prior.

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gibbs_cs_wish(formula, Kt = 5, data = NULL, verbose = TRUE,
  N.iter = 5000, N.burn = 1000, alpha = 0.1, min.iter = 10,
  max.iter = 50, Aw = NULL, Bw = NULL, v = NULL, SEED = NULL)

`formula`	a formula indicating the structure of the proposed model.
`Kt`	number of spline basis functions used to estimate coefficient functions
`data`	an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.
`verbose`	logical defaulting to `TRUE` – should updates on progress be printed?
`N.iter`	number of iterations used in the Gibbs sampler
`N.burn`	number of iterations discarded as burn-in
`alpha`	tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
`Aw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
`Bw`	hyperparameter for inverse gamma controlling variance of spline terms for population-level effects
`v`	hyperparameter for inverse Wishart prior on residual covariance
`seed`	seed value to start the sampler; ensures reproducibility