R: Using Percolation and Conductance to Find Information Flow Certainty in a Direct Network

# Using Percolation and Conductance to Find Information Flow Certainty in a Direct Network

## Documentation for package ‘Perc’ version 0.1.6

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

as.conflictmat | convert to a matrix of 'conf.mat' class
bradleyTerry | Computes the MLE for the BT model using an MM algorithm
bt.test | Systemic test for the assumptions of the Bradley-Terry model
conductance | compute win-loss probabilities
countPaths | count paths between all pairs
dyadicLongConverter | dyadic long format converter
findAllPaths | Identifies all paths between all pairs of less than or equal to a certain length
findIDpaths | find all paths of a certain length for an individual
getAllCosts | Associate each costs with its corresponding simulated annealing runs
getAllRankOrder | assign IDs to all best rank orders
getBestRankOrder | assign IDs to the best rank order
getSimOutput | get useful outputs from simulated annealing processes
individualDomProb | individual-level probability converter
Perc | Perc.
plotConfmat | generate heat map for a matrix
plotProbDiagnosis | Diagnosis Plot 'plotProbDiagnosis' generate heat map for dominance probability matrix
sampleEdgelist | sampleEdgelist. social interactions among 11 monkeys
sampleRawMatrix | sampleRawMatrix. dominance interactions between 39 monkeys
sampleWeightedEdgelist | sampleWeightedEdgelist. dominance interactions among 29 monkeys
simRankOrder | Find rank order using simulated annealing
transitivity | calculate transitivity measurements for a matrix
valueConverter | win-loss probability matrix value converter