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Material for the paper entitled "Splitting models for multivariate count data"

This repository contains supplementary material for the reproducibility of computational studies performed in the article "Splitting distributions for multivariate count data" written by:

• Pierre Fernique,
• Jean Peyhardi,
• Jean-Baptiste Durand.

This article has been pre-published in the arXiv e-print service. Here is the the citation formated as the bibtex standard.

@ARTICLE{2018arXiv180202074F,
         author = {{Fernique}, P. and {Peyhardi}, J. and {Durand}, J.-B.},
         title = "{Splitting models for multivariate count data}",
         journal = {ArXiv e-prints},
         archivePrefix = "arXiv",
         eprint = {1802.02074},
         primaryClass = "math.ST",
         keywords = {Mathematics - Statistics Theory, Mathematics - Probability},
         year = 2018,
         month = feb,
         adsurl = {http://adsabs.harvard.edu/abs/2018arXiv180202074F},
         adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

These studies are formatted as pre-executed Jupyter notebooks. Refers to the index.ipynb notebook which presents and references each study.

Install it !

You can install required packages on your computer to reproduce these studies. In order to ease the installation of these packages on multiple operating systems, the Conda package and environment management system is used. For more information refers to the StatisKit software suite documentation concerning prerequisites to the installation step. Then, to install the required packages, proceed as as follows:

1. Clone this repository,

   git clone --recursive https://github.com/StatisKit/FPD18

2. Create a Conda environment containing the meta-package fpd18,

   conda create -n fpd18 fpd18 -c statiskit -c r -c defaults --override-channels

3. Activate the Conda environment as advised in your terminal.

   conda activate fpd18

4. Enter the directory containing Jupyter notebooks,

   cd FPD18
   cd share
   cd jupyter

5. Launch the Jupyter the index.ipynb notebook,

   jupyter notebook index.ipynb

6. Execute the index.ipynb notebook to execute all examples or navigate among referenced notebooks to execute them separately.