/INAr

Generation, estimation and testing of INteger Autoregressive models

Primary LanguageRGNU General Public License v3.0GPL-3.0

INAr R Package

Generation, estimation and testing of INteger Autoregressive models

CRAN status R-CMD-check Project Status: Active - The project is being actively developed codecov License: GPL-3

Overview

This package 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.

Roadmap

Main Steps

  • Generation
    • Simulating INAR(p) process with different innovations
  • Estimation
    • YW and CLS estimation of INAR(p) processes with Poisson innovations
    • YW and CLS estimation of INAR(p) processes with Negative Binomial innovations
    • YW estimation of INAR(p) processes with other innovations (Good, Genaralized Poisson, Katz family, ...)
    • CML estimation of INAR(p) processes
    • Forecasting INAR(p) processes
  • Test
    • Semiparametric Bootstrap test
    • Parametric Bootstrap test - Poisson and Negative Binomial
    • Parametric Bootstrap test - Other distributions
  • Visualization
    • Summary
    • Plotting

Secondary Steps

  • Define package structure and state
    • 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

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")

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.

Ask a question ⁉️

Using INAr and got stuck? Browse the documentation to see if you can find a solution. Still stuck? Post your question as an issue on GitHub. While we cannot offer user support, we'll try to do our best to address it, as questions often lead to better documentation or the discovery of bugs.

Want to ask a question in private? Contact the package maintainer by email.

Propose an idea 💡

Have an idea for a new INAr feature? Take a look at the documentation and issue list to see if it isn't included or suggested yet. If not, suggest your idea as an issue on GitHub. While we can't promise to implement your idea, it helps to:

  • Explain in detail how it would work.
  • Keep the scope as narrow as possible.

See below if you want to contribute code for your idea as well. See also the Roadmap section above.

Report a bug 🐛

Using INAr and discovered a bug? That's annoying! Don't let others have the same experience and report it as an issue on GitHub so we can fix it. A good bug report makes it easier for us to do so, so please include:

  • Your operating system name and version (e.g. Mac OS 10.13.6).
  • Any details about your local setup that might be helpful in troubleshooting.
  • Detailed steps to reproduce the bug.

Care to fix bugs or implement new functionality for INAr? Awesome! 👏 Have a look at the issue list and leave a comment on the things you want to work on.

Improve the documentation 📖

Noticed a typo on the website? Think a function could use a better example? Good documentation makes all the difference, so your help to improve it is very welcome! Functions are described as comments near their code and translated to documentation using roxygen2. If you want to improve a function description:

  1. Go to R/ directory in the code repository.
  2. Look for the file with the name of the function.
  3. Propose a file change to update the function documentation in the roxygen comments (starting with #').

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.