timeawarepc 2.0.0

Time-Aware PC Python Package

TimeAwarePC: A Python Package for Finding Causal Connectivity from Time Series Data

TimeAwarePC is a Python package that implements the Time-Aware PC Algorithm for finding the Causal Functional Connectivity from time series data, based on recent research in directed probabilistic graphical modeling with time series [1]. The package also includes implementations of Granger Causality and the PC algorithm.

Installation

Requirements

• Python >=3.7, <3.11
• Python packages automatically checked and installed as part of the setup. To use Granger Causality, additional dependency of nitime which can be installed by pip install nitime.
• R == 4.4.2
• R package kpcalg and its dependencies. They can be installed in R or RStudio as follows:

> install.packages("BiocManager")
> BiocManager::install("graph")
> BiocManager::install("RBGL")
> install.packages("pcalg")
> install.packages("https://cran.r-project.org/src/contrib/Archive/kpcalg/kpcalg_1.0.1.tar.gz")

After meeting these requirements, you can get the latest version of TimeAwarePC as follows.

$ pip install timeawarepc

Documentation

Documentation is available at readthedocs.org

Tutorial

See the Quick Start Guide for a quick tutorial of the main functionalities of this library and check if it is installed properly.

What's new in v2.0.0 (BREAKING)

• cfc_tpc now defaults to no bootstrap subsampling: a single PC run is performed on the full time-delayed data.
  • To enable the legacy bootstrap behavior (50-row windows, 25 iterations), pass subsampsize=50, niter=25 explicitly.
  • Both subsampsize and niter must be specified together (or both left as the default None).
• partial_corr now fits an intercept and is shift-invariant. Previously the regression was forced through the origin, biasing residuals when the data was not mean-centered.
• See CHANGELOG.md for the full list of changes and migration notes.

Contributing

Your help is absolutely welcome! Please do reach out or create a feature branch!

Citation

Biswas, R., & Shlizerman, E. (2022). Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC Algorithm. https://doi.org/10.1371/journal.pcbi.1010653

Biswas, R., & Shlizerman, E. (2021). Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study. Frontiers in Systems Neuroscience. https://doi.org/10.3389/fnsys.2022.817962

References

R Clay Reid. (2012) From functional architecture to functional connectomics. Neuron, 75(2):209–217.

Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T. E., ... & Woolrich, M. W. (2011). Network modelling methods for FMRI. Neuroimage, 54(2), 875-891.

Judea Pearl. (2009) Causality. Cambridge University press.

Markus Kalisch and Peter Bhlmann. (2007) Estimating high-dimensional directed acyclic graphs with the pc-algorithm. In The Journal of Machine Learning Research, Vol. 8, pp. 613-636.

Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. (2000) Causation, prediction, and search. MIT press.