INAr R Project

Generation, estimation and testing of INteger Autoregressive models

Overview

The INAr Project aims to provide a set of tools for the study of time series having a discrete support by using the integer-valued autoregressive models, namely INAR(p), considered the counterpart to the conventional autoregressive models AR(p). INAR(p) models are proved to useful for the study of realizations of random variables arising from counting, with range contained in the discrete set of non-negative integers.

Installation

# Install from CRAN 
# !!!---not available at the moment---!!!
# install.packages("INAr")

# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("blog-neas/INAr")

Roadmap

Main Steps

The project considers to distribute a set of packages for the study of INAR(p) processes, which aim to provide tools for the generation, estimation and testing of these models. The following steps are planned for the future:

1. Score Tests

• Sun & McCabe Test
  • Semiparametric Bootstrap test
  • Parametric Bootstrap test - Poisson, Negative Binomial and Generalized Poisson
  • Parametric Bootstrap test - Other distributions
• Harris & McCabe Test
  • Semiparametric Bootstrap test
  • Parametric Bootstrap test - Poisson, Negative Binomial and Generalized Poisson
  • Parametric Bootstrap test - Other distributions

2. INAR model fitting, estimation and forecast

• Generation
  • Simulating INAR(p) process with different innovations
• Estimation
  • YW and CLS estimation of INAR(p) processes with Poisson and Negative Binomial innovations
  • YW estimation of INAR(p) processes with other innovations (Good, Generalized Poisson, Katz family, ...)
  • CML estimation of INAR(p) processes
  • Forecasting INAR(p) processes
• Visualization
  • Summary
  • Plotting

Secondary Steps

• Define package structures and states
  • Functions
  • Dependencies list
• Licensing: GPL-3
• Testing
• Documentation
  • Function documentation
  • Vignettes
• Maintenance and distribution
  • Continuous integration
  • Releasing to CRAN
  • Lifecycle
  • References
• Further steps and developments

Contributing to INAr development

First of all, thanks for considering contributing to INAr! 👍 INAr is an open source project maintained by people who care, and an help is always appreciated. 😊

There are several ways you can contribute to this project.

• Think INAr is useful? Let others discover it, by telling them in person, via Twitter or a blog post.

• Using INAr for a paper you are writing? Consider citing it.

• Did you discover a bug? That's annoying! Don't let others have the same experience and report it as an issue on GitHub

• Have an idea for a new INAr feature? Suggest it as an issue on GitHub.

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Please note that this project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.