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far

R package for modelization of Functional AutoRegressive processes

In collaboration with Serge Guillas, I write a paper called Estimation and simulation of autoregressive Hilbertian processes with exogenous variables which introduced application of ARH models, also known as FAR (Functional AutoRegressive processes).

We write this library during this work and decided to freely distribute it as this work is now finished.

This library include modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel,estimation of the covariance operator in a subspace, ...

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Version

Install

install.packages('far')

Version

0.6-7

License

LGPL-2.1

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Last Published

September 17th, 2024

Functions in far (0.6-7)

plot.fdata

Plot Functional Data
is.na.fdata

Not Available / ``Missing'' Values
multplot

Multivariate plots
orthonormalization

Orthonormalization of a set of a matrix
kerfon

Functional Kernel estimation
maxfdata

Maxima of functional data
select.fdata

Subscript of fdata
pred.persist

Forecasting using functional persistence
predict.kerfon

Forecasting of functional kernel model
simul.farx

FARX(1) process simulation
predict.far

Forecasting of FARX(1) model
simul.far.sde

FAR-SDE process simulation
simul.wiener

Wiener process simulation
simul.far.wiener

FAR(1) process simulation with Wiener noise
simul.far

FAR(1) process simulation
date.fdata

Extract the date of fdata
interpol.matrix

Interpolation matrix
BaseK2BaseC

Changing Basis
fapply

Apply functions over a fdata object
invgen

Generalized inverse of a Matrix
base.simul.far

Creating functional basis
coef.far

Extract Model Coefficients
fdata

Functional Data class
far.cv

Cross Validation for FARX(1) model
far

FARX(1) model estimation