BoolFilter v1.0.0

This is the initial release of BoolFilter, a package intended to provide tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems (POBDS).

Features

• BKF() provides the original Boolean Kalman Filter described by Braga-Neto et al.
• BKS() provides implementation of the Bolean Kalman Smoother algorithm, which is the optimal solution for a POBDS of finite data
• SIR-BKF() provides an SIR Particle Filtering approach to approximation of an optimal estimate of a high-dimensional PODBS, in which the BKF() estimation could prove computationally inefficient
• simulateNetwork() allows for simulation of Boolean Networks with various possible transition noise magnitudes and observation noise models to create test datasets for the POBDS algorithms provided in this package
• plotTrajectory() allows for easy visualization of individual gene data, with the added functionality of overlaying multiple datasets for a comparison

Authors

• Levi D. McClenny, M.S Electrical Engineering (package maintainer), Texas A&M University
• Mahdi Imani, Ph. D. candidate Electrical Engineering, Texas A&M University
• Dr. Ulisses Braga-Neto, Electrical Engineering faculty, Texas A&M University