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fsemipar (version 1.1.1)

Estimation, Variable Selection and Prediction for Functional Semiparametric Models

Description

Routines for the estimation or simultaneous estimation and variable selection in several functional semiparametric models with scalar responses are provided. These models include the functional single-index model, the semi-functional partial linear model, and the semi-functional partial linear single-index model. Additionally, the package offers algorithms for handling scalar covariates with linear effects that originate from the discretization of a curve. This functionality is applicable in the context of the linear model, the multi-functional partial linear model, and the multi-functional partial linear single-index model.

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Version

Install

install.packages('fsemipar')

Monthly Downloads

179

Version

1.1.1

License

GPL (>= 2)

Maintainer

Silvia Novo

Last Published

May 21st, 2024

Functions in fsemipar (1.1.1)

fsemipar-package

tools:::Rd_package_title("fsemipar")
Sugar

Sugar data
IASSMR.kernel.fit

Impact point selection with IASSMR and kernel estimation
IASSMR.kNN.fit

Impact point selection with IASSMR and kNN estimation
PVS.fit

Impact point selection with PVS
FASSMR.kNN.fit

Impact point selection with FASSMR and kNN estimation
PVS.kernel.fit

Impact point selection with PVS and kernel estimation
PVS.kNN.fit

Impact point selection with PVS and kNN estimation
Tecator

Tecator data
FASSMR.kernel.fit

Impact point selection with FASSMR and kernel estimation
fsim.kNN.fit.optim

Functional single-index model fit using kNN estimation and iterative LOOCV minimisation
fsemipar.internal

Package fsemipar internal functions
predict.IASSMR

Prediction for MFPLSIM
fsim.kNN.fit

Functional single-index model fit using kNN estimation and joint LOOCV minimisation
fsim.kernel.fit.optim

Functional single-index model fit using kernel estimation and iterative LOOCV minimisation
fsim.kernel.test

Functional single-index kernel predictor
fsim.kernel.fit

Functional single-index model fit using kernel estimation and joint LOOCV minimisation
fsim.kNN.test

Functional single-index kNN predictor
plot.classes

Graphical representation of regression model outputs
lm.pels.fit

Regularised fit of sparse linear regression
print.summary.sfpl

Summarise information from SFPLM estimation
predict.sfpl

Predictions for SFPLM
predict.lm

Prediction for linear models
print.summary.mfplsim

Summarise information from MFPLSIM estimation
print.summary.fsim

Summarise information from FSIM estimation
predict.sfplsim.FASSMR

Prediction for SFPLSIM and MFPLSIM (using FASSMR)
print.summary.mfpl

Summarise information from MFPLM estimation
predict.fsim

Prediction for FSIM
predict.mfplm.PVS

Prediction for MFPLM
print.summary.lm

Summarise information from linear models estimation
sfplsim.kNN.fit

SFPLSIM regularised fit using kNN estimation
print.summary.sfplsim

Summarise information from SFPLSIM estimation
sfpl.kernel.fit

SFPLM regularised fit using kernel estimation
semimetric.projec

Projection semi-metric computation
sfplsim.kernel.fit

SFPLSIM regularised fit using kernel estimation
projec

Inner product computation
sfpl.kNN.fit

SFPLM regularised fit using kNN estimation