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deal (version 1.1-15)
Learning Bayesian Networks with Mixed Variables
Description
Bayesian networks with continuous and/or discrete variables can be learned and compared from data.
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Install
install.packages('deal')
Monthly Downloads
447
Version
1.1-15
License
GPL version 2 or newer
Maintainer
Claus Dethlefsen
Last Published
November 9th, 2022
Functions in deal (1.1-15)
Search all functions
findex
Translation between indices in a multiway array
genlatex
From a network family, generate LaTeX output
cycletest
Test if network contains a cycle
drawnetwork
Graphical interface for manipulation of networks
autosearch
Greedy search
addarrow
Adding/Turning/Removing arrows
line
Prints a line of symbols
makesimprob
Make a suggestion for simulation probabilities
conditional
Calculate conditional distribution
ksl
Health and social characteristics
node
Nodes
numbermixed
The number of possible networks
networkfamily
Generates and learns all networks for a set of variables.
addarrows
Add arrows to/from node
rats
Weightloss of rats
nwfunique
Makes a network family unique.
localmaster
Local master
jointprior
Calculates the joint prior and the quantities needed to specify local parameter priors
postdist
Calculate mean of posterior parameters and create probability distribution
savenet
Saves Bayesian network as .net file
addrandomarrow
Adding/Turning/Removing random arrows
elementin
Is a network element in a list of networks?
nwfsort
Sorts a list of networks
learn
Estimates the parameters in the local probability distributions from data
post
Calculation of parameter posteriors for continuous node
maketrylist
Creates the full trylist
perturb
Perturbs a network
simulation
Simulation of data sets with a given dependency structure
network
Bayesian network data structure
readnet
Reads .net file
insert
Insert/remove an arrow in network
nwequal
Test if the graphs of two networks are equal