Bayesian Network Structure Learning from Data with Missing Values. The package implements the Silander-Myllymaki complete search, the Max-Min Parents-and-Children, the Hill-Climbing, the Max-Min Hill-climbing heuristic searches, and the Structural Expectation-Maximization algorithm. Available scoring functions are BDeu, AIC, BIC. The package also implements methods for generating and using bootstrap samples, imputed data, inference.
R package for Bayesian Network Structure Learning from Data with Missing Values
Bayesian Networks are a powerful tool for
probabilistic inference among a set of variables, modeled using a
directed acyclic graph. However, one often does not have the network,
but only a set of observations, and wants to reconstruct the network
that generated the data. The
bnstruct package provides
objects and methods for learning the structure and parameters of the
network in various situations, such as in presence of missing data, for
which it is possible to perform imputation (guessing the missing
values, by looking at the data). The package also contains methods for
learning using the Bootstrap technique. Finally,
bnstruct, has a set of additional tools to use Bayesian
Networks, such as methods to perform belief propagation.
In particular, the absence of some observations in the dataset is a very
common situation in real-life applications such as biology or medicine,
but very few software around is devoted to address these problems.
bnstruct is developed mainly with the purpose of filling
The latest stable version of
bnstruct is available
and can be installed with
from within an R session.
The latest development version of
bnstruct can be found on GitHub
In order to install the package, it suffices to launch
R CMD INSTALL path/to/bnstruct
from a terminal, or
make install from within the package source folder.
Being hosted on GitHub, it is also possible to use the
tool from an R session:
For Windows platforms, a binary executable of the latest stable version is available on CRAN.
bnstruct requires R
>= 2.10, and depends on
Rgraphviz is requested in
order to plot graphs, but is not mandatory.