Bayesian inference for dynamic functiononscalars regression, where a functional data response is regressed on scalar predictors. Here, both the functional response and the scalar predictors may be timeordered. The functions are modeled nonparametrically using an unknown basis expansion, which is learned from the data. The regression coefficients themselves are functions, and may be dynamic as well. The model is represented using a state space construction, which allows for timevarying parameter regression and autocorrelated errors.
Package details 


Author  Daniel R. Kowal <[email protected]> 
Maintainer  Daniel R. Kowal <[email protected]> 
License  GPL3 
Version  0.1.0 
Package repository  View on GitHub 
Installation 
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