Modeling Count Time Series Data via Gaussian Copula Models
ARMA Correlation Structure for Copula Time Series
Extract Coefficients from a gctsc Model
Set Options for Gaussian Copula Time Series Model
Fit a Gaussian Copula Time Series Model for Count Data
Marginal Models for Copula Time Series
Diagnostic Plots for Fitted Gaussian Copula Time Series Models
Approximate Log-Likelihood via Continuous Extension (CE)
GHK Log-Likelihood Approximation
TMET Log-Likelihood Approximation
Predictive Distribution and Scoring for Gaussian Copula Time Series Mo...
Print a gctsc Model Object
Print Summary of a gctsc Model
Compute Randomized Quantile Residuals for Gaussian Copula Time Series
Simulate from Gaussian Copula Time Series Models
Summarize a gctsc Model Fit
Gaussian copula models for count time series. Includes simulation utilities, likelihood approximation, maximum-likelihood estimation, residual diagnostics, and predictive inference. Implements the Time Series Minimax Exponential Tilting (TMET) method, an adaptation of Minimax Exponential Tilting (Botev, 2017) <doi:10.1111/rssb.12162> and the Vecchia-based tilting framework of Cao and Katzfuss (2025) <doi:10.1080/01621459.2025.2546586>. Also provides a linear-cost implementation of the Geweke–Hajivassiliou–Keane (GHK) simulator inspired by 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 structure of 'gcmr', but all code in 'gctsc' was developed independently.