gctsc: Gaussian and Student-t Copula Models for Count Time Series
Provides likelihood-based inference for Gaussian and Student-t
copula models for univariate count time series. Supports Poisson,
negative binomial, binomial, beta-binomial, and zero-inflated
marginals with ARMA dependence structures. Includes simulation,
maximum-likelihood estimation, residual diagnostics, and predictive
inference. Implements Time Series Minimax Exponential Tilting (TMET)
<doi:10.1016/j.csda.2026.108344>, an adaptation of minimax exponential
tilting of Botev (2017) <doi:10.1111/rssb.12162>. Also provides a
linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK)
simulator following Masarotto and Varin (2012) <doi:10.1214/12-EJS721>,
and the Continuous Extension (CE) approximation of Nguyen and
De Oliveira (2025) <doi:10.1080/02664763.2025.2498502>. The package
follows the S3 design philosophy of 'gcmr' but is developed independently.
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