fdlm_flc: Factor Loading Curve Sampling Algorithm
In drkowal/FDLM: A Bayesian Multivariate Functional Dynamic Linear Model

Sample the factor loading curve basis coefficients subject to an orthonormality constraint. Additional linear constraints may be included as well.

1	fdlm_flc(BtY, Beta, Psi, BtB, Omega, BtCon = NULL, lambda, sigmat2)

`BtY`	`J x T` matrix `B.t()*Y` for basis matrix B
`Beta`	`T x K` matrix of factors
`Psi`	`J x K` matrix of previous factor loading curve coefficients
`BtB`	`J x J` matrix of `B.t()*B`
`Omega`	`J x J` prior precision/penalty matrix
`BtCon`	(optional) `J x Jc` matrix of additional constraints, pre-multiplied by B.t()
`lambda`	`K`-dimensional vector of prior precisions
`sigmat2`	`T`-dimensional vector of time-dependent observation error variances

Psi J x K matrix of factor loading curve coefficients

This is a wrapper for Rcpp functions for the special cases of K = 1 and whether or not additional (linear) constraints are included, i.e., whether or not BtCon is non-NULL.

drkowal/FDLM documentation built on May 20, 2019, 5:20 p.m.