Learn R Programming

'mixAR' is an R package for time series modelling with mixture autoregressive and related models.

Installing mixAR

Install the latest stable version from CRAN:

install.packages("mixAR")

Alternatively, install the development version of mixAR from Github:

remotes::install_github("GeoBosh/mixAR")

Overview

Package mixAR provides functions for modelling with mixture autoregressive (MAR/mixAR) models.

See, for example, the overview of mixAR and model fitting functions such as fit_mixAR and bayes_mixAR.

Copy Link

Version

Install

install.packages('mixAR')

Monthly Downloads

332

Version

0.22.8

License

GPL (>= 2)

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/GeoBosh/mixAR/ (devel)

Homepage

https://geobosh.github.io/mixAR/ (doc)

Maintainer

Georgi Boshnakov

Last Published

December 19th, 2023

Functions in mixAR (0.22.8)

RJMCMC move for AR order selection of mixture autoregressive models

companion_matrix

Create a companion matrix from a vector

Compute probabilities for the observations to belong to each of the components

Log-likelihood of MixAR models

Optimise scale parameters in MixARgen models

Update the scale parameters of MixAR models

Functions for the standard normal distribution

Create estimation templates from MixAR model objects

Check if a MixAR model is stable

get_edist-methods

Methods for function get_edist in package mixAR

Generalised inner product and methods for class "MixComp"

fit_mixARreg-methods

Fit time series regression models with mixture autoregressive residuals

fit_mixAR-methods

Fit mixture autoregressive models

MixAR models for examples and testing

initialize-methods

Methods for function initialize in package mixAR

Internal mixAR Functions

Create MixAR objects

require("mixAR") pd <- packageDescription("mixAR") lb <- library(help="mixAR", character.only=TRUE) lbinfo <- if(!is.null(lb$info[[2]])) lb$info[[2]] else "No_matches" anypat <- function(string, pat, ...){ any(sapply(pat, function(x) grepl(x, string, ...))) } lbsubset <- function(lbinfo, pat){ if(length(lbinfo) == 0) return("No entries") ind.start <- which(sapply(lbinfo, function(x) anypat(x, pat))) if(length(ind.start) == 0) return("No entries") ind.ws <- which(grepl("^[[:space:]]+", lbinfo)) res <- ind.start for(ind in ind.start){ while(any((ind+1) - ind.ws == 0)){ ind <- ind + 1 res <- c(res, ind) } } lbinfo[ sort(res) ] } lbpaste <- function(pat) paste("\\preformatted{", {wrk <- lbsubset(lbinfo,pat); wrk <- gsub("(^[^\\]?%)|(([^\\])%)", paste0("\\3", "\\\\", "%"), wrk); paste(if(length(wrk) > 0) wrk else "No entries", collapse="\n")}, "}", sep="") pd$Title

A posteriori relabelling of a Markov chain

fit_mixVAR-methods

Fit mixture vector autoregressive models

Extract the last n elements of a vector

mixARExperiment

Simulation experiments with MixAR models

Relabel the components of a MixAR model

mix_location-methods

Conditional mean of MixAR models

Simulate from MixAR models

Diagnostic checks for mixture autoregressive models

Internal functions for estimation of MixAR models with Gaussian components

Fit mixture vector autoregressive models

Generator functions for noise distributions

mix_central_moment-methods

Methods for mix_central_moment

Vector of parameters of a MixAR model

mix_moment-methods

Methods for mix_moment

mixARemFixedPoint

EM estimation for mixture autoregressive models

Fit mixture autoregressive models with seasonal AR parameters

mix_pdf-methods

Conditional pdf's and cdf's of MixAR models

Conditional quantile functions of MixAR models

Function and methods to compute component residuals for MixAR models

lik_params_bounds

Give natural limits for parameters of a MixAR model.

Compute component predictions for MixAR models

M-step for models from class MixARgen

Calculate marginal loglikelihood at high density points of a MAR model.

make_fcond_lik-methods

Create a function for computation of conditional likelihood

Filter time series with MixAR filters

raggedCoefS-class

Class "raggedCoefS" --- ragged list

BIC based model selection for MixAR models

mixAR_cond_probs

The E-step of the EM algorithm for MixAR models

Simulate white noise series from a list of functions and vector of regimes

Conditional moments of MixAR models

Convert a ragged list into a matrix of characters

raggedCoefV-class

Class "raggedCoefV" --- ragged list

mix_ncomp-methods

Number of rows or columns of a MixComp object

noise_dist-methods

Methods for function noise_dist in package mixAR

Support for EM estimation of MixAR models, internal function.

Simulate from multivariate MixAR models

Filter a time series with options to shift and scale

multiStep_dist-methods

Multi-step predictions for MixAR models

Compute standard errors of estimates of MixAR models

row_lengths-methods

Methods for function row_lengths in package mixAR

Sampling functions for Bayesian analysis of mixture autoregressive models

mix_variance-methods

Methods for mix_variance

noise_rand-methods

Methods for function noise_rand in package mixAR

Internal mixAR functions

Set or extract the parameters of MixAR objects

randomArCoefficients

Random initial values for MixAR estimation

Infix operator to apply functions to matrix-like objects

raggedCoef-class

Class "raggedCoef" --- ragged list objects

noise_params-methods

Methods for function noise_params in package mixAR

noise_moment-methods

Compute moments of the noise components

Small utilities for ragged objects

Exact predictive parameters for multi-step MixAR prediction

A test for 'unswitch'

Show differences between two models

Perform simulation experiments

All permutations of the columns of a matrix

tomarparambyComp

Translations of my old MixAR Mathematica functions

Compute moments and absolute moments of standardised-t and normal distributions

Utility function for mixAR

Estimate probabilities of a MixAR model from tau.

Put core MixAR coefficients into a canonical form, internal function

Dealing with label switching in MixAR experiments

Class "MixVAR" --- mixture vector autoregressive models

Bayesian sampling of mixture autoregressive models

Adjust the length of the second argument to be the same as that of the first one

Choose the autoregressive order of MixAR components

Class "MixAR" --- mixture autoregressive models

Closing prices of four stocks

Class "MixARgen"

MixARGaussian-class

mixAR models with Gaussian noise components

Class "MixComp" --- manipulation of MixAR time series

Gaussian EM-step with random initialisation

MixVARGaussian-class

MixVAR models with multivariate Gaussian noise components

Utility function for MixAR

Calculate component specific error terms under MixAR model