dfosr_basis: MCMC Sampling Algorithm for the Dynamic Function-on-Scalars...
In drkowal/dfosr: Dynamic Function-on-Scalars Regression

Runs the MCMC for the dynamic function-on-scalars regression model based on a known basis expansion (splines or functional principal components). Here, we assume the factor regression has AR(1) errors.

dfosr_basis(Y, tau, X = NULL, use_dynamic_reg = TRUE, basis = "fpc",
  K = NULL, pve = 0.99, use_shrinkage_priors = FALSE, nsave = 1000,
  nburn = 1000, nskip = 2, mcmc_params = list("beta", "fk", "alpha",
  "mu_k", "ar_phi"), X_Tp1 = 1, use_obs_SV = FALSE)

`Y`	the `T x m` data observation matrix, where `T` is the number of time points and `m` is the number of observation points (`NA`s allowed)
`tau`	the `m x d` matrix of coordinates of observation points
`X`	the `T x p` matrix of predictors; if NULL, only include an intercept
`use_dynamic_reg`	logical; if TRUE, regression coefficients are dynamic (with random walk models)
`basis`	the functional basis to use, which will be orthogonalized; must be one of "fpc", "spline", or "ns" (Nelson-Siegel)
`K`	the number of FPCs to use; if NULL, select to explain `pve` (ignored for other basis functions)
`pve`	proportion of variability explained for the FPC basis; only used if `K` is NULL
`use_shrinkage_priors`	logical; if TRUE, include shrinkage priors for the coefficients
`nsave`	number of MCMC iterations to record
`nburn`	number of MCMC iterations to discard (burin-in)
`nskip`	number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw
`mcmc_params`	named list of parameters for which we store the MCMC output; must be one or more of "beta" (dynamic factors) "fk" (loading curves) "alpha" (regression coefficients; possibly dynamic) "mu_k" (intercept term for factor k) "ar_phi" (AR coefficients for each k under AR(1) model) "sigma_et" (observation error SD; possibly dynamic) "Yhat" (fitted values) "Yfore" (one-step forecast; includes the estimate and the distribution)
`X_Tp1`	the `p x 1` matrix of predictors at the forecasting time point `T + 1`
`use_obs_SV`	logical; when TRUE, include a stochastic volatility model for the observation error variance