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glober (version 1.0)

Estimating Functions with Multivariate B-Splines

Description

Generalized LassO applied to knot selection in multivariate B-splinE Regression (GLOBER) implements a novel approach for estimating functions in a multivariate nonparametric regression model based on an adaptive knot selection for B-splines using the Generalized Lasso. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2023), .

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Version

Install

install.packages('glober')

Monthly Downloads

135

Version

1.0

License

GPL-2

Maintainer

Mary Savino

Last Published

June 7th, 2023

Functions in glober (1.0)

glober-package

tools:::Rd_package_title("glober")
glober.1d

Estimation of functions with one input variable
xpred_1D

Values of the single input variable for which a function has to be estimated
f_1D

Output values of the evaluation of a function with one input variable and defined as a linear combination of B-splines
f_2D

Output values of the evaluation of a function with two input variables and defined as a linear combination of a tensor product of univariate B-splines
x_2D

Observation matrix x of two variables
glober.2d

Estimation of functions with two input variables
y_2D

Values of the response variable of the noisy observation set of two input variables
x_1D

Observation vector x of one variable
y_1D

Values of the response variable of the noisy observation set of one input variable
xpred_2D

Values of the two input variables for which a function has to be estimated