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deal (version 1.1-1)
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
550
Version
1.1-1
License
GPL version 2 or newer
Maintainer
Claus Dethlefsen
Last Published
November 9th, 2022
Functions in deal (1.1-1)
Search all functions
cycletest
Test if network contains a cycle
nwfunique
Thin a list of networks
jointprior
Setup a joint prior distribution for the parameters
network
Bayesian network data structure
drawnetwork
Graphical interface for manipulation of networks
simulation
Simulation of datasets with a given dependency structure
networkfamily
Collection of networks
findex
Translation between indices in a multiway array
maketrylist
Create the full trylist
postdist
Calculate mean of posterior parameters and create probability distribution
autosearch
Greedy search
addarrows
Add arrows to/from node
line
Prints a line of symbols
numbermixed
The number of distinct networks
addarrow
Adding/Turning/Removing arrows
post
Calculation of parameter posteriors for continuous node
elementin
Is a network element in a list of networks?
nwequal
Test if the graphs of two networks are equal
addrandomarrow
Adding/Turning/Removing random arrows
conditional
Calculate conditional distribution
savenet
Saves Bayesian network as .net file
splash
Show a splash screen
ksl
Health and social characteristics
perturb
Perturb a network
rats
Weightloss of rats
makesimprob
Make a suggestion for simulation probabilities
node
Nodes
nwfsort
Sort a list of networks
learn
Learning networks and nodes
insert
Insert/remove an arrow in network
readnet
Read .net file
localmaster
Local master
timeslice
Prepare time series data for analysis with Deal
genlatex
From a network family, generate LaTeX output
heuristic
Heuristic greedy search with random restart