Inform
Inform is a C library and collection of language wrappers, currently Python and R, for information-theoretic analysis of discrete-valued time series data (support for continuously-valued time series is in the works).
Each package was implemented with a suite of unit tests, and was designed to run on all major operating system (Windows, OS X and Linux). They all have live documentation and are open-source; contributions are welcomed. See each package's documentation or GitHub repository for installation instruction.
Each package was implemented with a suite of unit tests, and was designed to run on all major operating system (Windows, OS X and Linux). They all have live documentation and are open-source; contributions are welcomed. See each package's documentation or GitHub repository for installation instruction.
Package |
Langauge |
Version |
Source |
Documentation |
Inform |
C |
1.0.0 |
||
PyInform |
Python (2.7+/3.4+) |
0.6.0 |
||
rinform |
R |
1.0.0 |
Please cite any use of the above packages as:
- D. Moore, G. Valentini, S.I. Walker and M. Levin (2018) Inform: Efficient Information-theoretic Analysis of Collective Behaviors. Frontiers in Robotics and AI. doi: 10.3389/frobt.2018.00060
MaintainerS
Please feel free to contact the maintainers if you have any trouble installing or using Inform, or are interested in contributing.
Douglas G. Moore, Ph.D. (Inform, PyInform)
email: douglas.g.moore@asu.edu
website: https://dglmoore.com
github: https://github.com/dglmoore
email: douglas.g.moore@asu.edu
website: https://dglmoore.com
github: https://github.com/dglmoore
Gabriele Valentini, Ph.D. (rinform)
email: gvalent3@asu.edu
website: www.public.asu.edu/~gvalent3/
github: github.com/gvalentini85
email: gvalent3@asu.edu
website: www.public.asu.edu/~gvalent3/
github: github.com/gvalentini85
NEET
Neet is a package designed to analyze Boolean network models, particular models of gene regulatory systems. It currently supports both logic-based and weight-threshold networks and provides a suite of useful analyses including:
- Attractor landscape analysis
- Sensitivity analysis
- Informational architecture
Authors
Bryan C. Daniels, Bradley Karas, Hyunju Kim, Douglas G. Moore, Harrison Smith, Siyu Zhou and Sara I. Walker
Maintainter
Please feel free to contact the maintainer if you have any trouble installing or using Neet, or are interested in contributing.
Douglas G. Moore, Ph.D.
email: douglas.g.moore@asu.edu
website: https://dglmoore.com
github: https://github.com/dglmoore
email: douglas.g.moore@asu.edu
website: https://dglmoore.com
github: https://github.com/dglmoore