Facilities for easy implementation of hybrid Bayesian networks using R. Bayesian networks are directed acyclic graphs representing joint probability distributions, where each node represents a random variable and each edge represents conditionality. The full joint distribution is therefore factorized as a product of conditional densities, where each node is assumed to be independent of its non-descendents given information on its parent nodes. Since exact, closed-form algorithms are computationally burdensome for inference within hybrid networks that contain a combination of continuous and discrete nodes, particle-based approximation techniques like Markov Chain Monte Carlo are popular. We provide a user-friendly interface to constructing these networks and running inference using the 'rjags' package. Econometric analyses (maximum expected utility under competing policies, value of information) involving decision and utility nodes are also supported.
|Author||Jarrod E. Dalton <email@example.com> and Benjamin Nutter <firstname.lastname@example.org>|
|Date of publication||2017-01-13 15:26:14|
|Maintainer||Benjamin Nutter <email@example.com>|
|License||MIT + file LICENSE|
bindPosterior: Bind Posterior Distributions
BJDealer: Blackjack Dealer Outcome Probabilities
BlackJack: Black Jack Hybrid Decision Network
BlackJackTrain: Black Jack Network Training Dataset
chain: Chain together multiple operations.
compileDecisionModel: Compile JAGS Models to Evaluate the Effect of Decisions in a...
compileJagsModel: Compile Jags Model from a Hyde Network
cpt: Compute a conditional probability table for a factor given...
expectedVariables: List Expected Parameter Names and Expected Variables Names
factorFormula: Convert Factor Levels in Formula to Numeric Values
factorRegex: Produce Regular Expressions for Extracting Factor Names and...
HydeNetSummaries: HydeNet Summary Objects
HydeNetwork: Define a Probablistic Graphical Network
Hyde-package: Hydbrid Decision Networks
HydePosterior: Posterior Distributions of a Decision Network
HydeUtilities: Hyde Network Utility Functions
inputCPTExample: Example Conditional Probability Table Resulting from the...
jagsDists: JAGS Probability Distributions.
jagsFunctions: JAGS Functions Compatible with R.
mergeDefaultPlotOpts: rdname plot.HydeNetwork
modelToNode: Convert a Model Object to a Network Node
PE: Pulmonary Embolism Dataset
plot.HydeNetwork: Plotting Utilities Probabilistic Graphical Network
policyMatrix: Construct Policy and Decision Matrices
print.cpt: Print Method for CPT objects
print.HydeNetwork: Print a HydeNetwork
print.HydePosterior: Print a Hyde Posterior Distribution Object
Resolution.cpt: Conditional Probability Table for resolution of side effects...
rewriteHydeFormula: Rewrite HydeNetwork Graph Model Formula
SE.cpt: Conditional Probability Table for side effects as a function...
setDecisionNodes: Classify Multiple Nodes as Decision or Utility Nodes
setNode: Set Node Relationships
setNodeModels: Set Node Properties Using Model Objects
setPolicyValues: Assign Default Policy Values
TranslateFormula: Translate R Formula to JAGS
update.HydeNetwork: Update Probabilistic Graphical Network
vectorProbs: Convert a vector to JAGS Probabilities
writeJagsFormula: Write the JAGS Formula for a Hyde Node
writeJagsModel: Write a Node's JAGS Model
writeNetworkModel: Generate JAGS Code for a Network's Model