fsemipar: an R package for estimation, variable selection and prediction for functional semiparametric models

S. Novo Díaz, G. Aneiros Pérez

This package is devoted to estimation or simultaneous estimation and variable selection of several functional semiparametric models with a scalar response. That includes the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. It also contains algorithms for addressing estimation and variable selection in the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model when the scalar covariates with linear effects come from the discretization of a curve. In addition, the package has routines for kernel- and kNN-based estimation with Nadaraya-Watson weights of models with a nonparametric component. It also contains functions to compute predictions from all the considered models and estimation procedures.

Keywords: R package, Functional data analysis, Variable selection, Semiparametrics, Multi-functional covariates, Functional single-index model

GT09.NOPAR1 Invited Session. High dimension inference
November 7, 2023  3:30 PM
CC2: Conference Room

E. García Portugués, A. Meilán Vila

L. Freijeiro González, W. González Manteiga, M. Febrero Bande

A. Lago, I. Van Keilegom, J. C. Pardo Fernández, J. de Uña Álvarez