ltsa v1.4.6


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Linear Time Series Analysis

Methods of developing linear time series modelling. Methods are given for loglikelihood computation, forecasting and simulation.

Functions in ltsa

Name Description
DLAcfToAR Autocorrelations to AR parameters
DLResiduals Prediction residuals
TrenchInverse compute the matrix inverse of a positive-definite Toepliz matrix
TrenchMean Exact MLE for mean given the autocorrelation function
PredictionVariance Prediction variance
innovationVariance Nonparametric estimate of the innovation variance
DLLoglikelihood Durbin-Levinsion Loglikelihood
TrenchLoglikelihood Loglikelihood function of stationary time series using Trench algorithm
SimGLP Simulate GLP given innovations
ToeplitzInverseUpdate Inverse of Toeplitz matrix of order n+1 given inverse of order n
TrenchForecast Minimum Mean Square Forecast
is.toeplitz test if argument is a symmetric Toeplitz matrix
tacvfARMA theoretical autocovariance function (acvf) of ARMA
DHSimulate Simulate General Linear Process
exactLoglikelihood Exact log-likelihood and MLE for variance
ltsa-package Linear Time Series Analysis
DLSimulate Simulate linear time series
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Last month downloads


Date 2015-12-20
Classification/ACM G.3, G.4, I.5.1
Classification/MSC 62M10, 91B84
License GPL (>= 2)
NeedsCompilation yes
Packaged 2015-12-21 02:25:34 UTC; IanMcLeod
Repository CRAN
Date/Publication 2015-12-21 08:55:04

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