Package: EXPAR 0.1.0

EXPAR: Fitting of Exponential Autoregressive (EXPAR) Model

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.

Authors:Saikath Das [aut, cre], Bishal Gurung [aut], Achal Lama [aut], KN Singh [aut]

EXPAR_0.1.0.tar.gz
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EXPAR_0.1.0.tgz(r-4.4-any)EXPAR_0.1.0.tgz(r-4.3-any)
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EXPAR.pdf |EXPAR.html✨
EXPAR/json (API)

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This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

5 exports 0.09 score 45 dependencies 734 downloads

Last updated 4 months agofrom:e527d3301a. Checks:OK: 7. Indexed: yes.

Exports:best_EXPARfit_EXPARforecast_EXPARinital_valoptimise_EXPAR

Dependencies:clicolorspacecurlfansifarverforecastfracdiffgenericsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclelmtestmagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgconfigquadprogquantmodR6RColorBrewerRcppRcppArmadillorlangscalestibbletimeDatetseriesTTRurcautf8vctrsviridisLitewithrxtszoo