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The description :an overview of the bnlearn r package: learning algorithms, conditional independence tests and network scores....
This report updates in 25-Aug-2018
Created Date: | 2009-03-30 |
Changed Date: | 2016-01-11 |
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Latitude: | 51.246768951416 |
Longitude: | 0.60681998729706 |
Country: | United Kingdom (GB) |
City: | Leeds |
Region: | England |
ISP: | Linode LLC |
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bnlearn - an r package for bayesian network learning and inference home page documentation examples research notes bayesian network repository about the author data & r code data & r code downloads current release: 4.3 [ link ] latest snapshot + bugfixes: 4.4-20180818 [ link ] from the r studio cran mirror: research impact: bnlearn is an r package for learning the graphical structure of bayesian networks, estimate their parameters and perform some useful inference. i was first released in 2007, it has been been under continuous development for more than 10 years (and still going strong). to get started and install the latest development snapshot type install.packages("http://www.bnlearn.com/releases/bnlearn_latest.tar.gz") in your r console. (more detailed installation instructions below.) bnlearn implements the following constraint-based structure learning algorithms : pc (the stable version); grow-shrink (gs); incremental association markov blanket (iamb); fast incremental association (fast-iamb); interleaved incremental association (inter-iamb); max-min parents & children (mmpc); semi-interleaved hiton-pc (si-hiton-pc); the following score-based structure learning algorithms : hill climbing (hc); tabu search (tabu); the following hybrid structure learning algorithms : max-min hill climbing (mmhc); general 2-phase restricted maximization (rsmax2); the following local discovery algorithms : chow-liu; aracne; and the following bayesian network classifiers : naive bayes; tree-augmented naive bayes (tan). discrete (multinomial) and continuous (multivariate normal) data sets are supported, both for structure and parameter learning. the latter can be performed using either maximum likelihood or bayesian estimators. each constraint-based algorithm can be used with several conditional independence tests: categorical data (multinomial distribution): mutual information (parametric, semiparametric and permutation tests); shrinkage-estimator for the mutual information; pearson's x 2 (parametric, semiparametric and permutation tests); ordinal data : jonckheere-terpstra (parametric and permutation tests); continuous data (multivariate normal distribution): linear correlation (parametric, semiparametric and permutation tests); fisher's z (parametric, semiparametric and permutation tests); mutual information (parametric, semiparametric and permutation tests); shrinkage-estimator for the mutual information; mixed data (conditional gaussian distribution): mutual information (parametric, semiparametric); and each score-based algorithm can be used with several score functions: categorical data (multinomial distribution): the multinomial log-likelihood; the akaike information criterion (aic); the bayesian information criterion (bic); a score equivalent dirichlet posterior density (bde); a sparse dirichlet posterior density (bds); a dirichlet posterior density based on jeffrey's prior (bdj); a modified bayesian dirichlet for mixtures of interventional and observational data; the locally averaged bde score (bdla); the k2 score; continuous data (multivariate normal distribution): the multivariate gaussian log-likelihood; the corresponding akaike information criterion (aic); the corresponding bayesian information criterion (bic); a score equivalent gaussian posterior density (bge); mixed data (conditional gaussian distribution): the conditional gaussian log-likelihood; the corresponding akaike information criterion (aic); the corresponding bayesian information criterion (bic). package installation bnlearn is available on cran and can be downloaded from its web page in the packages section ( here ). it can be installed with a simple: install.packages("bnlearn") development snapshots, which include bugfixes that will be incorporated in the cran release as well as new features, can be downloaded from the links above or installed with a simple: install.packages("http://www.bnlearn.com/releases/bnlearn_latest.tar.gz") the only suggested packages not hosted on cran are graph and rgraphviz , which can be installed from bioconductor : source("http://bioconductor.org/bioclite.r") bioclite(c("graph", "rgraphviz")) following the instructions present on this and this webpages. please also note that the grain package, while on cran, depends on packages that are on bioconductor both directly and through the grbase package, which depends on rbgl : source("http://bioconductor.org/bioclite.r") bioclite(c("graph", "rgraphviz", "rbgl")) install.packages("grain") © 2009-2018 marco scutari. the contents of this page are licensed under the creative commons attribution-share alike license .
http://www.bnlearn.com/book-crc
http://www.bnlearn.com/about
http://www.bnlearn.com/releases/bnlearn_4.4-20180818.tar.gz
http://www.bnlearn.com/10th-anniversary.html
http://www.bnlearn.com/bnrepository
http://www.bnlearn.com/research
http://www.bnlearn.com/book-user
http://www.bnlearn.com/examples
http://www.bnlearn.com/documentation
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