knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "README-" )
This package contains tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.
Deploy the package:
library('BoolFilter')
A few examples of the basic use of the package are shown below:
data(p53net_DNAdsb0) #Simulate data from a Bernoulli observation model data <- simulateNetwork(p53net_DNAdsb0, n.data = 100, p = 0.02, obsModel = list(type = "Bernoulli", p = 0.02)) #Derive an estimate of the network using a BKF approach Results <- BKF(data$Y, p53net_DNAdsb0, .02, obsModel = list(type = "Bernoulli", p = 0.02)) #View network approximation vs. correct trajectory plotTrajectory(Results$Xhat, labels = p53net_DNAdsb0$genes, dataset2 = data$X, compare = TRUE)
BoolFilter comes with capibilites for Multiple-Model Adaptive Estimation (citation in vignette), in which model selection and parameter estimation is made possible by implementing a bank Boolean Kalman Filters running in parallel.
data(p53net_DNAdsb1) net1 <- p53net_DNAdsb0 net2 <- p53net_DNAdsb1 #define observation model observation = list(type = 'NB', s = 10.875, mu = 0.01, delta = c(2, 2, 2, 2), phi = c(3, 3, 3, 3)) #simulate data using one of the networks and a given 'p' data <- simulateNetwork(net1, n.data = 100, p = 0.02, obsModel = observation) #run MMAE to determine model selection and parameter estimation MMAE(data, net=c("net1","net2"), p=c(0.02,0.1,0.15), threshold=0.8, obsModel = observation)
More information can be found in the packages vignette, including more detailed examples and explainations of the individual algorithms included in the package.
All references for the above can be found in the vignette references section.
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