Package index • mixAR

mixAR overview

mixAR-package: Mixture Autoregressive Models

EM and Bayesian estimation of mixture autoregressive models

Fit mixture autoregressive models using the EM algorithm or MCMC.

fit_mixAR(): Fit mixture autoregressive models

bayes_mixAR(): Bayesian sampling of mixture autoregressive models

fit_mixARreg() mixARreg(): Fit time series regression models with mixture autoregressive residuals

mixSARfit(): Fit mixture autoregressive models with seasonal AR parameters

tsdiag(<MixAR>) mixAR_diag(): Diagnostic checks for mixture autoregressive models

mix_se(): Compute standard errors of estimates of MixAR models

mixAR_BIC() BIC_comp(): BIC based model selection for MixAR models

Prediction

Predictive distributions and their summary statistics

mix_pdf mix_pdf-methods mix_pdf,MixARGaussian,missing,missing,numeric-method mix_pdf,MixARGaussian,numeric,missing,numeric-method mix_pdf,MixARGaussian,numeric,numeric,missing-method mix_pdf,MixARgen,missing,missing,numeric-method mix_pdf,MixARgen,numeric,missing,numeric-method mix_pdf,MixARgen,numeric,numeric,missing-method mix_cdf mix_cdf-methods mix_cdf,MixARGaussian,missing,missing,numeric-method mix_cdf,MixARGaussian,numeric,missing,numeric-method mix_cdf,MixARGaussian,numeric,numeric,missing-method mix_cdf,MixARgen,missing,missing,numeric-method mix_cdf,MixARgen,numeric,missing,numeric-method mix_cdf,MixARgen,numeric,numeric,missing-method: Conditional pdf's and cdf's of MixAR models

mix_qf(): Conditional quantile functions of MixAR models

mix_location() mix_variance() mix_central_moment() mix_moment() mix_kurtosis() mix_ekurtosis(): Conditional moments of MixAR models

multiStep_dist(): Multi-step predictions for MixAR models

Simulation of mixAR time series

mixARnoise_sim(): Simulate white noise series from a list of functions and vector of regimes

mixAR_sim() mixAny_sim(): Simulate from MixAR models

mixAR models

MixARGaussian-class MixARGaussian: mixAR models with Gaussian noise components

MixARgen-class mixARgen: Class "MixARgen"

MixAR-class: Class "MixAR" — mixture autoregressive models

mixAR(): Create MixAR objects

exampleModels: MixAR models for examples and testing

show_diff(): Show differences between two models

make_fcond_lik(): Create a function for computation of conditional likelihood

get_edist-methods get_edist,MixAR-method get_edist,MixARGaussian-method get_edist,MixARgen-method: Methods for function get_edist in package mixAR

mix_ncomp(): Number of rows or columns of a MixComp object

row_lengths-methods row_lengths row_lengths,ANY-method row_lengths,MixAR-method row_lengths,raggedCoef-method: Methods for function row_lengths in package mixAR

Multivariate mixAR (mixVAR) models

mixVAR_sim(): Simulate from multivariate MixAR models

mixVARfit(): Fit mixture vector autoregressive models

MixVAR-class: Class "MixVAR" — mixture vector autoregressive models

MixVARGaussian-class MixVARGaussian: MixVAR models with multivariate Gaussian noise components

fit_mixVAR(): Fit mixture vector autoregressive models

raggedCoefV-class raggedCoefV [,raggedCoefV,numeric,ANY-method [,raggedCoefV,numeric,ANY,ANY-method [,raggedCoefV,numeric,missing,ANY-method [,raggedCoefV,missing,numeric,ANY-method [,raggedCoefV,numeric,numeric,ANY-method [[,raggedCoefV,missing,ANY-method [[,raggedCoefV,numeric,ANY-method [,raggedCoefV,missing,ANY,ANY-method: Class "raggedCoefV" — ragged list

Bayesian utilities for mixAR models

Choose_pk(): Choose the autoregressive order of MixAR components

sampZpi() sampMuShift() sampSigmaTau(): Sampling functions for Bayesian analysis of mixture autoregressive models

bx_dx(): RJMCMC move for AR order selection of mixture autoregressive models

label_switch(): A posteriori relabelling of a Markov chain

Mixture distributions

Compute properties of mixture distributions.

dist_norm: Functions for the standard normal distribution

fdist_stdnorm() fdist_stdt() fn_stdt() b_show() distlist() ed_nparam ed_parse() ed_skeleton() ed_src ed_stdnorm ed_stdt ed_stdt0 ed_stdt1 ft_stdt: Generator functions for noise distributions

get_edist() noise_dist() noise_rand() noise_params() set_noise_params(): Internal mixAR functions

stdnormmoment() stdnormabsmoment() stdtmoment() stdtabsmoment() tabsmoment(): Compute moments and absolute moments of standardised-t and normal distributions

mixAR parameters

mixAR parameters.

parameters() `parameters<-`(): Set or extract the parameters of MixAR objects

isStable(): Check if a MixAR model is stable

lik_params(): Vector of parameters of a MixAR model

lik_params_bounds(): Give natural limits for parameters of a MixAR model.

mixAR_switch() mixAR_permute(): Relabel the components of a MixAR model

predict_coef(): Exact predictive parameters for multi-step MixAR prediction

tomarparambyComp() tomarparambyType() permuteArpar(): Translations of my old MixAR Mathematica functions

Operators and functions

Operators and functions for computations with components of mixAR models.

inner(): Generalised inner product and methods for class "MixComp"

`%of%`: Infix operator to apply functions to matrix-like objects

Computations with noise distributions of mixAR models

Computations involving noise distributions of mixAR models.

noise_dist-methods noise_dist,MixAR-method noise_dist,MixARGaussian-method noise_dist,MixARgen-method: Methods for function noise_dist in package mixAR

noise_moment(): Compute moments of the noise components

noise_params-methods noise_params,MixAR-method noise_params,MixARgen-method: Methods for function noise_params in package mixAR

noise_rand-methods noise_rand,MixAR-method noise_rand,MixARGaussian-method noise_rand,MixARgen-method: Methods for function noise_rand in package mixAR

Computations with components of mixAR models

Computations involving components of mixAR models.

MixComp-class dim,MixComp-method -,MixComp,missing-method -,MixComp,numeric-method -,numeric,MixComp-method *,character,MixComp-method *,function,MixComp-method *,MixComp,MixComp-method *,MixComp,numeric-method *,numeric,MixComp-method /,MixComp,numeric-method /,numeric,MixComp-method +,MixComp,numeric-method +,numeric,MixComp-method ^,MixComp,numeric-method: Class "MixComp" — manipulation of MixAR time series

mix_ek(): Function and methods to compute component residuals for MixAR models

mix_hatk(): Compute component predictions for MixAR models

raghat1(): Filter a time series with options to shift and scale

EM and other utilities for mixAR models

EM and other utilities for mixAR models.

em_est_dist(): Optimise scale parameters in MixARgen models

tauetk2sigmahat() em_est_sigma(): Update the scale parameters of MixAR models

em_rinit() etk2tau(): Gaussian EM-step with random initialisation

em_tau() em_tau_safe(): Compute probabilities for the observations to belong to each of the components

est_templ(): Create estimation templates from MixAR model objects

cond_loglik() cond_loglikS(): Log-likelihood of MixAR models

marg_loglik(): Calculate marginal loglikelihood at high density points of a MAR model.

mixAR_cond_probs(): The E-step of the EM algorithm for MixAR models

mixARemFixedPoint() mixARgenemFixedPoint(): EM estimation for mixture autoregressive models

mixgenMstep(): M-step for models from class MixARgen

tauCorrelate() tau2arcoef() mixMstep(): Internal functions for estimation of MixAR models with Gaussian components

mixFilter(): Filter time series with MixAR filters

mixSubsolve(): Support for EM estimation of MixAR models, internal function.

randomArCoefficients() randomMarParametersKernel() randomMarResiduals() tsDesignMatrixExtended(): Random initial values for MixAR estimation

tau2probhat(): Estimate probabilities of a MixAR model from tau.

Ragged coefficients

Ragged coefficients.

raggedCoef(): Class "raggedCoef" — ragged list objects

raggedCoefS-class raggedCoefS [,raggedCoefS,missing,missing,ANY-method [,raggedCoefS,numeric,missing,ANY-method [,raggedCoefS,missing,numeric,ANY-method [,raggedCoefS,numeric,numeric,ANY-method [[,raggedCoefS,ANY,missing-method [[,raggedCoefS,ANY,ANY-method [[ [[: Class "raggedCoefS" — ragged list

rag_modify() ragged2vec(): Small utilities for ragged objects

ragged2char(): Convert a ragged list into a matrix of characters

Datasets

PortfolioData1: Closing prices of four stocks

Other methods

mix_location-methods mix_location,MixAR,missing,missing,missing-method mix_location,MixAR,missing,missing,numeric-method mix_location,MixAR,numeric,missing,missing-method mix_location,MixAR,numeric,numeric,missing-method: Conditional mean of MixAR models

mix_moment-methods mix_moment,MixAR,missing,missing,numeric-method: Methods for mix_moment

mix_central_moment-methods mix_central_moment,MixAR,missing,missing,numeric-method: Methods for mix_central_moment

mix_variance-methods mix_variance,MixAR,missing,missing,missing-method mix_variance,MixAR,missing,missing,numeric-method mix_variance,MixAR,numeric,numeric,missing-method: Methods for mix_variance

Experimental (may be change/renamed/removed)

simuExperiment(): Perform simulation experiments

Internal utility functions

adjustLengths(): Adjust the length of the second argument to be the same as that of the first one

.canonic_coef(): Put core MixAR coefficients into a canonical form, internal function

companion_matrix(): Create a companion matrix from a vector

err_k(): Utility function for MixAR

err(): Calculate component specific error terms under MixAR model

initialize-methods initialize,MixAR-method initialize,raggedCoef-method initialize,raggedCoefS-method initialize,MixARgen-method initialize,raggedCoefV-method initialize,MixVAR-method: Methods for function initialize in package mixAR

lastn(): Extract the last n elements of a vector

permn_cols(): All permutations of the columns of a matrix

ui(): Utility function for mixAR