LatentGaussCounts

Modeling Integer-valued Time Series via Latent Gaussian Process

Installation

As the package is not yet on CRAN, it needs to be installed from GitHub:

library(devtools)  # if you don't have devtools: install.packages("devtools")
install_github("jlivsey/LatentGaussCounts")

Utility of LatentGaussCounts

• Fit count time series model for most classic discrete distributions
• Currently we support the following discrete dstributions
  • Poisson
  • mixture of Poissons
• Allow autocorrelation to depend on an underlying Gaussian process.
• Currently we support the following underlying Gaussian processes
  • AR(1)
  • FARIMA(0,d,0)
• Estimate model parameters with Gaussian likelihood of a particle filtering MLE.

Example

LGC(x, count.family = "Poisson",
       gauss.series = "AR", p=1,
       estim.method = "gaussianLik")

Authors

James Livsey (United States Census Bureau) and Vladas Pipiras(University of North Carolina, Chapel Hill)

License

TBD