## Classes

- The bn class structure
- The bn.fit class structure
- The bn.kcv class structure
- The bn.strength class structure

## Classifiers

## Convenience functions

- Get or create whitelists and blacklists
- Utilities to manipulate fitted Bayesian networks
- Construct configurations of discrete variables
- Miscellaneous utilities
- Build a model string from a Bayesian network and vice versa
- Partial node orderings
- Manipulating the test counter

## Data preprocessing

- Construct configurations of discrete variables
- Predict or impute missing data from a Bayesian network
- Pre-process data to better learn Bayesian networks

## Datasets

- ALARM monitoring system (synthetic) data set
- Asia (synthetic) data set by Lauritzen and Spiegelhalter
- Synthetic (mixed) data set to test learning algorithms
- Coronary heart disease data set
- Synthetic (continuous) data set to test learning algorithms
- The HailFinder weather forecast system (synthetic) data set
- Insurance evaluation network (synthetic) data set
- Synthetic (discrete) data set to test learning algorithms
- Lizards' perching behaviour data set
- Examination marks data set

## Graphs

- Drop, add or set the direction of an arc or an edge
- Compare two or more different Bayesian networks
- Count graphs with specific characteristics
- Equivalence classes, moral graphs and consistent extensions
- Test d-separation
- Utilities to manipulate graphs
- Miscellaneous utilities
- Manipulate nodes in a graph
- Partial node orderings

## Import/export to file

## Independence tests

## Inference

- Perform conditional probability queries
- Compute the distance between two fitted Bayesian networks
- Predict or impute missing data from a Bayesian network
- Simulate random samples from a given Bayesian network

## Interfaces to other packages

- Plot fitted Bayesian networks
- Compare two or more different Bayesian networks
- Import and export networks from the gRain package
- Import and export networks from the graph package
- Plotting networks with probability bars
- Advanced Bayesian network plots
- Import and export networks from the igraph package
- Import and export networks from the pcalg package
- Generating a prediction object for ROCR
- Produce lm objects from Bayesian networks
- Arc strength plot

## Local learning

## Missing data

## Network scores

- Estimate the optimal imaginary sample size for BDe(u)
- Measure arc strength
- Bayes factor between two network structures
- Utilities to manipulate fitted Bayesian networks
- Score of the Bayesian network

## Package

- Bayesian network Classifiers
- Conditional independence tests
- Network scores
- Structure learning algorithms
- Whitelists and blacklists in structure learning

## Parameter learning

- Fit the parameters of a Bayesian network
- Compute the distance between two fitted Bayesian networks
- Gaussian Bayesian networks and multivariate normals

## Plots

- Plot fitted Bayesian networks
- Compare two or more different Bayesian networks
- Plotting networks with probability bars
- Advanced Bayesian network plots
- Plot a Bayesian network
- Plot arc strengths derived from bootstrap
- Arc strength plot

## Resampling

## Simulation

- Perform conditional probability queries
- Generate empty, complete or random graphs
- Simulate random samples from a given Bayesian network

## Structure learning

- Estimate the optimal imaginary sample size for BDe(u)
- Measure arc strength
- Bayes factor between two network structures
- Get or create whitelists and blacklists
- Constraint-based structure learning algorithms
- Equivalence classes, moral graphs and consistent extensions
- Score-based structure learning algorithms
- Hybrid structure learning algorithms
- Naive Bayes classifiers
- Structure learning from missing data