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 GewekeHajivassiliouKeane (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.