Bayesian inference for dynamic function-on-scalars regression, where a functional data response is regressed on scalar predictors. Here, both the functional response and the scalar predictors may be time-ordered. 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 time-varying parameter regression and autocorrelated errors.
|Author||Daniel R. Kowal <[email protected]>|
|Maintainer||Daniel R. Kowal <[email protected]>|
|Package repository||View on GitHub|
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