FRegSigCom

R package FRegSigCom: Functional Regression using Signal Compression Approach. Signal compression methods for functional regression. It includes various function-on-function (FOF) regression models such as the linear FOF model with functional response and both scalar and functional predictors for a small number of functional predictors, linear FOF models with a large number of functional predictors, linear FOF model for spiky data, stepwise selection for FOF models with two-way interactions, and nonlinear FOF models. It also includes scalar-on-function regression models with single (SOF) or multivariate (mSOF) scalar response variable, and SOF model for spiky data.

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References in zbMATH (referenced in 3 articles , 1 standard article )

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  1. Beyaztas, Ufuk; Shang, Han Lin: A partial least squares approach for function-on-function interaction regression (2021)
  2. Zhou, Zhiyang: Fast implementation of partial least squares for function-on-function regression (2021)
  3. Qi, Xin; Luo, Ruiyan: Nonlinear function-on-function additive model with multiple predictor curves (2019)