Learning Bayesian Networks with the bnlearn R Package

By Marco Scutari. 2009

1,997 citations recorded by the source corpus.

bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010).

Subfields: Artificial Intelligence, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6a324ff29bca5cb4584ca869a9bab987fc8bed3f

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Citations in the corpus

Frequency in lexical processing (2016, methodology): “We made use of the max-min hill-climbing algorithm (Tsamardinos, Aliferis, & Statnikov, 2003) as implemented in the bnlearn package (Scutari, 2010) for R (R Development Core Team, 2015) to estimate the essential graph (also known as the completed partial directed acyclic graph), setting α to…”
Decreased Bacterial Diversity Characterizes an Altered Gut Microbiota in Psoriatic Arthritis and Resembles Dysbiosis of Inflammatory Bowel Disease (2015, methodology): “The optimal Bayesian network structure was inferred through the “high climbing” algorithm implemented in the bnlearn R package (18).”
Partition MCMC for Inference on Acyclic Digraphs (2015, methodology): “, 2012) and bnlearn (Scutari, 2010) in R, for constraint based learning methods, publicly available tools for MCMC based structure learning algorithms are less common, with the possible exception of BDAGL (Eaton and Murphy, 2007a).”
Bridge Centrality: A Network Approach to Understanding Comorbidity (2019, methodology): “DAGs can be estimated via several techniques (e.g., Kalisch, M€achler, Colombo, Maathuis, & B€uhlmann, 2012; Scutari, 2010).”
Can network analysis transform psychopathology? (2016, methodology): “(Scutari, 2010, p. 2) or prediction.”
Unpacking Rumination and Executive Control: A Network Perspective (2017, methodology): “We used a Bayesian hill-climbing algorithm implemented via the R package bnlearn (Scutari, 2010).”
DAG-based attack and defense modeling: Don't miss the forest for the attack trees (2013, methodology): “A third tool, tailored to statistical learning with Bayesian networks is bnlearn [259].”
SAM: Structural Agnostic Model, Causal Discovery and Penalized Adversarial Learning (2018, methodology): “MMHC is implemented with the bnlearn package (Scutari, 2009).”
On Structure Priors for Learning Bayesian Networks (2019, methodology): “Not all state-of-the-art software packages (Scutari 2010) allow the user to specify a structure prior; they offer a limited number of pre-implemented scoring functions, which assume a uniform prior.”
Causal Structure Learning (2017, methodology): “All methods are called through the interface oﬀered by the CompareCausalNetworks package which depends on the packages backShift (Heinze-Deml, 2017), bnlearn (Scutari, 2010) and pcalg (Kalisch et al., 2012) for the code of the considered methods.”
Causal Inference Using Graphical Models with the R Package pcalg (2012, methodology): “the Modified PC algorithm of Spirtes et al. (2000). The argument conservative can be used to invoke the conservative FCI algorithm which consists of two parts.”
A review on machine learning principles for multi-view biological data integration (2016, neutral): “networks bnlearn [159] Bayesian network learning and”
High-dimensional consistency in score-based and hybrid structure learning (2015, methodology): “We estimate the CPDAG-skeleton for ARGES-skeleton using the MMPC algorithm of Tsamardinos, Brown and Aliferis (2006), where we use the implementation in the R-package bnlearn [Scutari (2010)].”
D-VAE: A Variational Autoencoder for Directed Acyclic Graphs (2019, methodology): “Our Bayesian network dataset contains 200,000 random 8-node Bayesian networks from the bnlearn package [57] in R.”
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “MMHC is implemented with the bnlearn package (Scutari, 2009).”
What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis? (2020, methodology): “In particular, we considered its similarity to one of the examples from Pourret et al. (2008).”