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deal (version 1.2-3)
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
395
Version
1.2-3
License
GPL version 2 or newer
Maintainer
Claus Dethlefsen
Last Published
November 9th, 2022
Functions in deal (1.2-3)
Search all functions
addarrow
Adding/Turning/Removing arrows
makesimprob
Make a suggestion for simulation probabilities
unique.networkfamily
Makes a network family unique.
network
Bayesian network data structure
findex
Translation between indices in a multiway array
nwfsort
Sorts a list of networks
node
Representation of nodes
perturb
Perturbs a network
readnet
Reads/saves .net file
maketrylist
Creates the full trylist
Network tools
Tools for manipulating networks
learn
Estimation of parameters in the local probability distributions
autosearch
Greedy search
deal-internal
deal internal functions
elementin
Is a network element in a list of networks?
insert
Insert/remove an arrow in network
line
Prints a line of symbols
nwequal
Test if the graphs of two networks are equal
post
Calculation of parameter posteriors for continuous node
drawnetwork
Graphical interface for editing networks
score
Network score
prob
Local probability distributions
genlatex
From a network family, generate LaTeX output
networkfamily
Generates and learns all networks for a set of variables.
jointprior
Calculates the joint prior distribution
numbermixed
The number of possible networks
rnetwork
Simulation of data sets with a given dependency structure
addarrows
Add arrows to/from node
conditional
Calculate conditional distribution
localmaster
Local master
postdist
Calculate point estimate of posterior parameters and create probability distribution
addrandomarrow
Adding/Turning/Removing random arrows
cycletest
Test if network contains a cycle
simulation
Simulation of data sets with a given dependency structure
ksl
Health and social characteristics
rats
Weightloss of rats