Bayesian networks with continuous and/or discrete variables can be learned and compared from data.
|Author||Susanne Gammelgaard Bottcher, Claus Dethlefsen.|
|Date of publication||2013-01-28 17:47:26|
|Maintainer||Claus Dethlefsen <email@example.com>|
|License||GPL (>= 2)|
autosearch: Greedy search
deal-internal: deal internal functions
drawnetwork: Graphical interface for editing networks
genlatex: From a network family, generate LaTeX output
insert: Insert/remove an arrow in network
jointprior: Calculates the joint prior distribution
ksl: Health and social characteristics
learn: Estimation of parameters in the local probability...
makesimprob: Make a suggestion for simulation probabilities
maketrylist: Creates the full trylist
network: Bayesian network data structure
networkfamily: Generates and learns all networks for a set of variables.
networktools: Tools for manipulating networks
node: Representation of nodes
numbermixed: The number of possible networks
nwfsort: Sorts a list of networks
nwfunique: Makes a network family unique.
perturb: Perturbs a network
prob: Local probability distributions
rats: Weightloss of rats
readnet: Reads/saves .net file
rnetwork: Simulation of data sets with a given dependency structure
score: Network score