CampaRi - An R package for time-series analysis

CampaRi is a package for the analysis of time-series. Initially conceived as an R wrapper of the original campari software package, it has been subsequently extended with other techniques.
Most of the methods here implemented rely on the so-called Progress Index algorithm and SAPPHIRE (States And Pathways Projected with HIgh REsolution) plot; among them, network inference pre-processing (NetSAP), SAPPHIRE-based clustering (SbC).
See References for detailed information on the methods.
Please use the tutorials, vignettes or the function documentation to explore all of the possibilities.

The documentation of the package is available online on CampaRi doc

For any help with the package usage and installation please chat with us.

Installation

• Stable version: we will upload the package on CRAN as soon as possible.

• Development version: use devtools::install_gitlab("CaflischLab/CampaRi", dependencies = "Depends") to install the package directly from the repository, along with its fundamental dependencies

For further details on the installation procedure we suggest to read the installation tutorial.

Help and Tutorials

We are currently writing how-to-use guides with examples and datasets. Tutorial and vignettes can be also accessed on this website.

Extensive Manual

For full information on the Progress Index algorithm use, please refer to the main documentation of the original CAMPARI documentation. Some good starting points to understand and utilize campari syntax are the tutorials 1, 10 and 11.

Citations and references

Please cite the software as

• Garolini, D., Cocina, F. & Langini, C. CampaRi: an R package for time series analysis. (2019). doi:10.5281/zenodo.3428933

For the Progress Index algorithm and SAPPHIRE plot cite:

• Blöchliger, N., Vitalis, A., & Caflisch, A. (2013). A scalable algorithm to order and annotate continuous observations reveals the metastable states visited by dynamical systems. Comput. Phys. Commun. 184, 2446–2453. http://dx.doi.org/10.1016/j.cpc.2013.06.009
• Blöchliger, N., Vitalis, A., & Caflisch, A. (2014). High-resolution visualisation of the states and pathways sampled in molecular dynamics simulations. Sci. Rep. 4, 6264. https://doi.org/10.1038/srep06264

If using NetSAP method:

• Garolini, D., Vitalis, A., & Caflisch, A. (2019). Unsupervised identification of states from voltage recordings of neural networks. J. Neurosci. Methods, 318, 104–117. https://doi.org/10.1016/j.jneumeth.2019.01.019

Eventually, if using SAPPHIRE-based clustering:

• Cocina, F., Vitalis, A., & Caflisch, A. (2020). SAPPHIRE-Based Clustering. J. Chem. Theory Comput.. https://doi.org/10.1021/acs.jctc.0c00604