gctsc

gctsc

gctsc provides fast and scalable likelihood inference for Gaussian copula models for count time series, supporting a wide range of marginals:

• Poisson
  
• Negative Binomial
  
• Binomial
  
• Beta Binomial
• Zero-Inflated Poisson (ZIP)
  
• Zero-Inflated Binomial (ZIB)
  
• Zero-Inflated Beta-Binomial (ZIBB)

The package implements several likelihood approximation methods — including the proposed
TMET (Time Series Minimax Exponential Tilting) and GHK — and exploits the ARMA dependence structure for efficient high-dimensional computation.

Additional features include simulation utilities, residual diagnostic tools, and one-step prediction.

Reference

If you use this package in published work, please cite:

Nguyen, N. & De Oliveira, V. (2025).
Likelihood Inference in Gaussian Copula Models for Count Time Series via Minimax Exponential Tilting.