An R Package: Digitised Gaussian copula ARMA Models for Integer-valued time series
Maintainer: Hannah Lennon
Contact: drhannahlennon@gmail.com
Last Updated: 23 March 2021
The R package copulaIVTS provides all statistical R used to implement a Monte Carlo Expectation Maximisation algorithm for maximum likelihood estimation of the dependence parameters of a digitised Gaussian copula ARMA model. Useful for count time series also.
Details are described in the manuscript "Estimation of a digitised Gaussian ARMA model by Monte Carlo Expectation Maximisation",
available at https://doi.org/10.1016/j.csda.2018.10.015
and further described in PhD Thesis format here https://www.research.manchester.ac.uk.
To be be added to the R package shortly, there are two alternative estimation methods to fit the digitised Gaussian ARMA model provided in the Thesis link;
- via Approximate Bayesian Computation (ABC) and
- via approximate MLE via a d-vine bivariate copula representation.
Count time series, Discrete time series, integer-valued time series, ARMA dependence structure
To cite package ‘copulaIVTS’ in publications use:
Hannah Lennon (2021). copulaIVTS: Copula models for integer-valued
time series. R package version 1.2.
Lennon H., & Yuan J., Estimation of a digitised Gaussian ARMA model by Monte Carlo Expectation Maximisation, Computational Statistics & Data Analysis 2018;8:e020683
Lennon, H., 2016. Gaussian copula modelling for integer-valued time series (Doctoral Thesis, University of Manchester).
I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I prefer you use the Github issues system over trying to reach out to me in other ways. Pull requests for contributions are encouraged.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.