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weakARMA

The goal of weakARMA is to allows the study of nonlinear time series models through weak ARMA representations.

Installation (Gitlab)

Current released

You can install the released version of weakARMA from PLMlab with:

install.packages("remotes")
remotes::install_gitlab("jrolland/weakARMA", host="https://plmlab.math.cnrs.fr")

Development version

You can install the currently developed version of weakARMA from PLMlab with:

install.packages("remotes")
remotes::install_git("https://plmlab.math.cnrs.fr/jrolland/weakARMA.git", ref="develop")

Installation (CRAN)

CRAN package is available. You can install the released version of weakARMA from CRAN with:

install.packages("weakARMA")

Example

This is a basic example which shows you how to solve a common problem:

library(weakARMA)
## basic example code

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Version

Install

install.packages('weakARMA')

Monthly Downloads

180

Version

1.0.3

License

GPL (>= 3)

Maintainer

Julien Yves Rolland

Last Published

April 4th, 2022

Functions in weakARMA (1.0.3)

CAC40

Paris stock exchange
acf.univ

Computation of autocovariance and autocorrelation for an ARMA residuals.
omega

Computation of Fisher information matrice
portmanteauTest.h

Portmanteau tests for one lag.
estimation

Parameters estimation of a time series.
signifparam

Computes the parameters significance
nl.acf

Autocorrelogram
meansq

Function optim will minimize
portmanteauTest

Portmanteau tests
wnRT

Weak white noise
CAC40return.sq

Paris stock exchange square return
CAC40return

Paris stock exchange return
sim.ARMA

Simulation of ARMA(p,q) model.
simGARCH

GARCH process
acf.gamma_m

Computation of autocovariance and autocorrelation for an ARMA residuals.
wnPT_SQ

Weak white noise
VARest

Estimation of VAR(p) model
wnPT

Weak white noise
gradient

Computation the gradient of the residuals of an ARMA model
matXi

Estimation of Fisher information matrix I
ARMA.selec

Selection of ARMA models