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