README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)
```
[![Build Status](https://travis-ci.org/levimcclenny/BoolFilter.svg?branch=master)](https://travis-ci.org/levimcclenny/pkg)

#Description

This package contains tools for optimal and approximate state estimation as well as network inference of Partially-Observed Boolean Dynamical Systems.

#Use
Deploy the package:
```{r}
library('BoolFilter')
```

A few examples of the basic use of the package are shown below:

###Data Generation, Filtering, and visualization
```{r}
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)
```

###Model Selection
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.


```{r}
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
More information can be found in the packages vignette, including more detailed examples and explainations of the individual algorithms included in the package.


#References
All references for the above can be found in the vignette references section.