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Update R-bnlearn to version 3.7.1. Changes: bnlearn (3.7.1) * small changes to make CRAN check happy. bnlearn (3.7) * fixed the default setting for the number of particles in cpquery() (thanks Nishanth Upadhyaya). * reimplemented common test patterns in monolithic C functions to speed up constraint-based algorithms. * added support for conditional linear Gaussian (CLG) networks. * fixed several recursion bugs in choose.direction(). * make read.{bif,dsc,net}() consistent with the `$<-` method for bn.fit objects (thanks Felix Rios). * support empty networks in read.{bif,dsc,net}(). * fixed bug in hc(), triggered when using both random restarts and the maxp argument (thanks Irene Kaplow). * correctly initialize the Castelo & Siebes prior (thanks Irene Kaplow). * change the prior distribution for the training variable in classifiers from the uniform prior to the fitted distribution in the bn.fit.{naive,tan} object, for consistency with gRain and e1071 (thanks Bojan Mihaljevic). * note AIC and BIC scaling in the documentation (thanks Thomas Lefevre). * note limitations of {white,black}lists in tree.bayes() (thanks Bojan Mihaljevic). * better input sanitization in custom.fit() and bn.fit<-(). * fixed .Call stack imbalance in random restarts (thanks James Jensen). * note limitations of predict()ing from bn objects (thanks Florian Sieck). bnlearn (3.6) * support rectangular nodes in {graphviz,strength}.plot(). * fixed bug in hc(), random restarts occasionally introduced cycles in the graph (thanks Boris Freydin). * handle ordinal networks in as.grain(), treat variables as categorical (thanks Yannis Haralambous). * discretize() returns unordered factors for backward compatibility. * added write.dot() to export network structures as DOT files. * added mutual information and X^2 tests with adjusted degrees of freedom. * default vstruct() and cpdag() to moral = FALSE (thanks Jean-Baptiste Denis). * implemented posterior predictions in predict() using likelihood weighting. * prevent silent reuse of AIC penalization coefficient when computing BIC and vice versa (thanks MarГ­a Luisa Matey). * added a "bn.cpdist" class and a "method" attribute to the random data generated by cpdist(). * attach the weights to the return value of cpdist(..., method = "lw"). * changed the default number of simulations in cp{query, dist}(). * support interval and multiple-valued evidence for likelihood weighting in cp{query,dist}(). * implemented dedup() to pre-process continuous data. * fixed a scalability bug in blacklist sanitization (thanks Dong Yeon Cho). * fixed permutation test support in relevant(). * reimplemented the conditional.test() backend completely in C for speed, it is now called indep.test().
2015-03-02 14:31:25 +00:00
Bayesian network structure learning, parameter learning and inference.
Author provides a perfect overview in the package description. Sync to it.
2012-08-29 21:40:37 +00:00
Update R-bnlearn to version 3.7.1. Changes: bnlearn (3.7.1) * small changes to make CRAN check happy. bnlearn (3.7) * fixed the default setting for the number of particles in cpquery() (thanks Nishanth Upadhyaya). * reimplemented common test patterns in monolithic C functions to speed up constraint-based algorithms. * added support for conditional linear Gaussian (CLG) networks. * fixed several recursion bugs in choose.direction(). * make read.{bif,dsc,net}() consistent with the `$<-` method for bn.fit objects (thanks Felix Rios). * support empty networks in read.{bif,dsc,net}(). * fixed bug in hc(), triggered when using both random restarts and the maxp argument (thanks Irene Kaplow). * correctly initialize the Castelo & Siebes prior (thanks Irene Kaplow). * change the prior distribution for the training variable in classifiers from the uniform prior to the fitted distribution in the bn.fit.{naive,tan} object, for consistency with gRain and e1071 (thanks Bojan Mihaljevic). * note AIC and BIC scaling in the documentation (thanks Thomas Lefevre). * note limitations of {white,black}lists in tree.bayes() (thanks Bojan Mihaljevic). * better input sanitization in custom.fit() and bn.fit<-(). * fixed .Call stack imbalance in random restarts (thanks James Jensen). * note limitations of predict()ing from bn objects (thanks Florian Sieck). bnlearn (3.6) * support rectangular nodes in {graphviz,strength}.plot(). * fixed bug in hc(), random restarts occasionally introduced cycles in the graph (thanks Boris Freydin). * handle ordinal networks in as.grain(), treat variables as categorical (thanks Yannis Haralambous). * discretize() returns unordered factors for backward compatibility. * added write.dot() to export network structures as DOT files. * added mutual information and X^2 tests with adjusted degrees of freedom. * default vstruct() and cpdag() to moral = FALSE (thanks Jean-Baptiste Denis). * implemented posterior predictions in predict() using likelihood weighting. * prevent silent reuse of AIC penalization coefficient when computing BIC and vice versa (thanks MarГ­a Luisa Matey). * added a "bn.cpdist" class and a "method" attribute to the random data generated by cpdist(). * attach the weights to the return value of cpdist(..., method = "lw"). * changed the default number of simulations in cp{query, dist}(). * support interval and multiple-valued evidence for likelihood weighting in cp{query,dist}(). * implemented dedup() to pre-process continuous data. * fixed a scalability bug in blacklist sanitization (thanks Dong Yeon Cho). * fixed permutation test support in relevant(). * reimplemented the conditional.test() backend completely in C for speed, it is now called indep.test().
2015-03-02 14:31:25 +00:00
This package implements constraint-based (GS, IAMB, Inter-IAMB,
Fast-IAMB, MMPC, Hiton-PC), pairwise (ARACNE and Chow-Liu),
score-based (Hill-Climbing and Tabu Search) and hybrid (MMHC and
RSMAX2) structure learning algorithms for discrete, Gaussian and
conditional Gaussian networks, along with many score functions and
conditional independence tests. The Naive Bayes and the Tree-Augmented
Naive Bayes (TAN) classifiers are also implemented.
Author provides a perfect overview in the package description. Sync to it.
2012-08-29 21:40:37 +00:00
Some utility functions (model comparison and manipulation, random
data generation, arc orientation testing, simple and advanced plots)
Update R-bnlearn to version 3.7.1. Changes: bnlearn (3.7.1) * small changes to make CRAN check happy. bnlearn (3.7) * fixed the default setting for the number of particles in cpquery() (thanks Nishanth Upadhyaya). * reimplemented common test patterns in monolithic C functions to speed up constraint-based algorithms. * added support for conditional linear Gaussian (CLG) networks. * fixed several recursion bugs in choose.direction(). * make read.{bif,dsc,net}() consistent with the `$<-` method for bn.fit objects (thanks Felix Rios). * support empty networks in read.{bif,dsc,net}(). * fixed bug in hc(), triggered when using both random restarts and the maxp argument (thanks Irene Kaplow). * correctly initialize the Castelo & Siebes prior (thanks Irene Kaplow). * change the prior distribution for the training variable in classifiers from the uniform prior to the fitted distribution in the bn.fit.{naive,tan} object, for consistency with gRain and e1071 (thanks Bojan Mihaljevic). * note AIC and BIC scaling in the documentation (thanks Thomas Lefevre). * note limitations of {white,black}lists in tree.bayes() (thanks Bojan Mihaljevic). * better input sanitization in custom.fit() and bn.fit<-(). * fixed .Call stack imbalance in random restarts (thanks James Jensen). * note limitations of predict()ing from bn objects (thanks Florian Sieck). bnlearn (3.6) * support rectangular nodes in {graphviz,strength}.plot(). * fixed bug in hc(), random restarts occasionally introduced cycles in the graph (thanks Boris Freydin). * handle ordinal networks in as.grain(), treat variables as categorical (thanks Yannis Haralambous). * discretize() returns unordered factors for backward compatibility. * added write.dot() to export network structures as DOT files. * added mutual information and X^2 tests with adjusted degrees of freedom. * default vstruct() and cpdag() to moral = FALSE (thanks Jean-Baptiste Denis). * implemented posterior predictions in predict() using likelihood weighting. * prevent silent reuse of AIC penalization coefficient when computing BIC and vice versa (thanks MarГ­a Luisa Matey). * added a "bn.cpdist" class and a "method" attribute to the random data generated by cpdist(). * attach the weights to the return value of cpdist(..., method = "lw"). * changed the default number of simulations in cp{query, dist}(). * support interval and multiple-valued evidence for likelihood weighting in cp{query,dist}(). * implemented dedup() to pre-process continuous data. * fixed a scalability bug in blacklist sanitization (thanks Dong Yeon Cho). * fixed permutation test support in relevant(). * reimplemented the conditional.test() backend completely in C for speed, it is now called indep.test().
2015-03-02 14:31:25 +00:00
are included, as well as support for parameter estimation (maximum
likelihood and Bayesian) and inference, conditional probability
queries and cross-validation.