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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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install.packages('far')

Monthly Downloads

401

Version

0.6-6

License

LGPL-2.1

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Maintainer

Damon Julien Guillas Serge

Last Published

August 13th, 2022

Functions in far (0.6-6)

far.cv

Cross Validation for FARX(1) model
invgen

Generalized inverse of a Matrix
fapply

Apply functions over a fdata object
base.simul.far

Creating functional basis
interpol.matrix

Interpolation matrix
fdata

Functional Data class
BaseK2BaseC

Changing Basis
coef.far

Extract Model Coefficients
far

FARX(1) model estimation
predict.kerfon

Forecasting of functional kernel model
date.fdata

Extract the date of fdata
select.fdata

Subscript of fdata
orthonormalization

Orthonormalization of a set of a matrix
is.na.fdata

Not Available / ``Missing'' Values
simul.far

FAR(1) process simulation
kerfon

Functional Kernel estimation
simul.far.wiener

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

FARX(1) process simulation
predict.far

Forecasting of FARX(1) model
pred.persist

Forecasting using functional persistence
plot.fdata

Plot Functional Data
simul.wiener

Wiener process simulation
simul.far.sde

FAR-SDE process simulation
maxfdata

Maxima of functional data
multplot

Multivariate plots