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.