bnlearn (3.0) * added dsep() to test d-separation. * implemented Tree-Augmented Naive Bayes (TAN) in tree.bayes(). * implemented Semi-Interleaved HITON-PC in si.hiton.pc(). * improved parsing in read.{bif,dsc,net}(). * fixed an indexing error in Gaussian permutation tests. * added a "textCol" highlight option to graphviz.plot(). * added {incoming,outgoing,incident}.arcs(). * fixed two hard-to hit bugs in TABU search (thanks Maxime Gasse). * added custom.fit() for expert parameter specification. * added support for Markov blanket preseeding in learn.mb(). * assume independence instead of returning an error when a partial correlation test fails due to errors in the computation of the pseudoinverse of the covariance matrix. * fixed an incorrect optimization in the backward phase of mmpc(). * fixed a floating point rounding problem in the mutual information tests (64bit OSes only, thanks Maxime Gasse). * fixed cp{query,dist}() handling of TRUE (thanks Maxime Gasse). * added learn.nbr() to complement learn.mb(). bnlearn (2.9) * integrate with the graph package and provide import/export functions for the graphNEL and graphAM classes. * removed bn.var.test() and the "aict" test. * fixed wrong ISS handling in the BDe score. * fixed a buffer overflow in the BGe score. * added subgraph(), tiers2blacklist(), and cextend(). * fixed bug in boot.strength(), which failed with a spurious error when a PDAG was learned. * support interval discretization as the initial step in Hartemink's discretization. * fixed blacklist handling in chow.liu(). * fixed choose.direction() and arc.strength(), both of them require a .test.counter and should create it as needed. * added as.bn() and as.bn.fit() for "grain" objects (from the the gRain package) and as.grain() for "bn.fit" objects. * fixed infinte loop in choose.direction(). * make choose.direction(..., criterion = "bootstrap") work again. * added an 'every' argument to random.graph() for the 'ic-dag' and 'melancon' algorithms. * shortened the optional arguments for random.graph(..., method = "hartemink") to "idisc" and "ibreaks". bnlearn (2.8) * switched "cpdag" to TRUE in {boot,custom}.strength(). * added a "weights" argument to custom.strength(). * implemented the modified BDe score handling mixtures of experimental and observational data (mbde). * reimplemented the BGe score for speed (about 20x faster). * fixed a buffer overflow in predict() for discrete networks. * fixed sanitization in predict() for bn.fit.{d,g}node objects. * handle partially directed graphs in bn.cv() for log-likelihood loss (both discrete and Gaussian). bnlearn (2.7) * make .onLoad() more robust, so that it passes "R CMD check" even with a broken Graphviz installation. * reduced memory usage in graphviz.plot(), now using arc lists instead of adjacency matrices. * added tie breaking in prediction. * allow inter.iamb() to break infinite loops instead of returning an error. * fixed a buffer overflow in discrete Monte Carlo tests. * added sequential Monte Carlo permutation tests. * improved performance and error handling in Gaussian Monte Carlo tests. bnlearn (2.6) * allow discrete data in Hartemink's discretization algorithm. * implemented {read,write}.bif() to import/export BIF files. * implemented {read,write}.dsc() to import/export DSC files. * implemented {read,write}.net() to import/export NET files. * completely reimplemented compare() to return useful metrics, it was just a slower version of all.equal(). * implemented model averaging with significance thresholding in averaged.network(). * use new significance thresholding in {boot,custom}.strength() and arc.strength(criterion = "bootstrap"). * export predicted values in bn.cv() when using classification error. * fixed an integer overflow in mutual information tests. bnlearn (2.5) * reimplemented rbn.discrete() in C both for speed and to get CPT indexing right this time. * added bn.net() to complement bn.fit(). * changed the default value of the imaginary sample size to 10, which is a de facto standard in literature. * implemented the ARACNE and Chow-Liu learning algorithms. * improved robustness of correlation estimation. * added a "cpdag" option to boot.strength(). * fixed bug in discretize(). * improved sanitization in graphviz.plot() and strength.plot(). * added Hamming distance. bnlearn (2.4) * reimplemented naive Bayes prediction in C for speed. * added some debugging output to predict() methods. * fixed printing of fitted Gaussian Bayesian networks. * fixed stack imbalance in Gaussian Monte Carlo tests. * implemented some discretization methods in discretize(). * added custom.strength() for arbitrary sets of networks. * fixed strength.plot() threshold for significant arcs. bnlearn (2.3) * added cpdist() to generate observations from arbitrary conditional probability distributions. * added a simple naive.bayes() implementation for discrete networks, complete with a predict() implementation using maximum posterior probability. * added the shrinkage test for the Gaussian mutual information. * added ntests(), in.degree(), out.degree(), degree(), whitelist() and blacklist(). * added support for the snow package in bn.boot(), bn.cv(), cpquery() and cpdist(). * fixed integer overflow in the computation of the number of parameters of discrete networks. * fixed errors in the labels of Gaussian scores. bnlearn (2.2) * fixed a bug in moral(), which returned duplicated arcs when shielded parents were present in the graph. * implemented all.equal() for bn objects. * added workaround for plotting empty graphs with graphviz.plot(), which previously generated an error in Rgraphviz. * added a CITATION file. * added the narcs() function. * print.bn()'s now supports small(er) line widths. * added support for "bn.fit" objects in graphviz.plot(). * added support for changing the line type of arcs in graphviz.plot(). * added the learn.mb() function to learn the Markov blanket of a single variable. * fixed calls to UseMethod(), which were not always working correctly because of the changed parameter matching. * fixed an off-by-one error in the prediction for discrete root nodes. bnlearn (2.1) * optimized data frame subsetting and parents' configurations construction in conditional.test() and score.delta(). * fixed and improved the performance of rbn(). * fixed wrong penalization coefficient for Gaussian AIC (was computing a Gaussian BIC instead). * added cpquery() to perform conditional probability queries via Logic (Rejection) Sampling. * added bn.cv() to perform k-fold cross-validation, with expected log-likelihood and classification error as loss functions. * added predict(), logLik() and AIC() methods for bn.fit objects. * renamed bnboot() to bn.boot() for consistency with bn.cv() and bn.fit(). bnlearn (2.0) * added the shd() distance. * renamed dag2ug() to skeleton(), which is more intuitive. * added support for "bn.fit" objects in rbn(). * added vstructs() and moral(). * added the coronary data set. * improved partial correlation resillience to floating point errors when dealing with ill-behaved covariance matrices. * miscellaneous (small) optimizations in both R and C code. bnlearn (1.9) * added support for "bn.fit" objects in nodes(), nbr(), parents(), children(), root.nodes(), leaf.nodes(), arcs(), directed.arcs(), undirected.arcs(), amat(), nparams(), mb(), path(), directed(), acyclic(), node.ordering(). * fixed bug in hybrid and score-based learning algorithms, which did not handle blacklists correctly. bnlearn (1.8) * removed the fast mutual information test in favour of the equivalent shrinkage test, which uses a more systematic approach. * fixed fast.iamb(), which should not have truncated exact and Monte Carlo tests. * added the HailFinder and Insurance data sets. * updated the Grow-Shrink implementation according to newer (and much clearer) literature from Margaritis. * rewritten more of the configuration() function in C, resulting in dramatic (2x to 3x) speedups for large data sets. * implemented tabu search. * removed rshc() in favour of rsmax2(), a general two-stage restricted maximization hybrid learning algorithm. * reimplemented cpdag() in C, with an eye towards a future integration with constraints-based algorithms. * fixed a bug in coef() for discrete bn.fit objects. * implemented Melancon's uniform probability random DAG generation algorithm. bnlearn (1.7) * big clean-up of C code, with some small optimizations. * fixed bug in the handling of upper triangular matrices (UPTRI3 macro in C code). * added the dag2ug() and pdag2dag() functions. * fixed a bug in bn.fit(), now it really works even for discrete data. * added bn.moments(), bn.var() and bn.var.test() for basic probabilistic modelling of network structures. bnlearn (1.6) * implemented the mmhc() algorithm and its generic template rshc(). * rewritten both the optimized and the standard implementation of hc() in C, they are way faster than before. * various fixes to documentation and bibtex references. * revised the functions implementing the second half of the constraint-based algorithm. * improved parameter sanitization in "amat<-"(). * fixed the functions that set arcs' direction in constraint-based algorithms. bnlearn (1.5) * improved parameter sanitization in the "<-"() functions and modelstring(). * added support for bootstrap inference with bnboot(), boot.strength(), arc.strength(, criterion = "bootstrap") and choose.direction(, criterion = "bootstrap"). * fixed a bug in acyclic() causing false negatives. * added bn.fit() for estimating the parameters of a Bayesian network conditional on its structure. * mapped some S3 methods (print, fitted, fitted.values, residuals, resid, coefs, coefficients) to objects of class "bn.fit", "bn.fit.gnode" and "bn.fit.dnode". * added some plots for the fitted models based on the lattice package. * implemented AIC and BIC for continuous data, and removed the likelihood score. * various optimizations to C code. * throughout documentation update. * fixed an infinite loop bug in inter.iamb(). bnlearn (1.4) * exported the "B" parameter to specify the number of permutations to be done in a permutation test. * removed the "direction" parameter from constraint-based learning algorithms, as it was non-standard, misnamed and had various reported problems. * removed the duplicate "dir" label for the BDe score. * added support for Gaussian data to rbn() and nparams(). * added "modelstring<-"(). * revised references in documentation. * added the alarm and marks data sets. * moved the scripts to generate data from the networks included as data sets to the "network.scripts" directory. bnlearn (1.3) * added Monte Carlo permutation tests for mutual information (for both discrete and Gaussian data), Pearson's X^2, linear correlation and Fisher's Z. * added strength.plot(). * reimplemented random.graph() in C for speed. * clean up of C memory allocation functions. bnlearn (1.2) * added cache.partial.structure() to selectively update nodes' cached information stored in 'bn' objects. * fixed a bug in cache.structure(). * reimplemented is.acyclic() in C to fix a bug causing false negatives. * added the lizards data set. bnlearn (1.1) * implemented mmpc(). * slightly changed gaussian.test to be more learning-friendly. * fixed bugs in empty.graph() and "arcs<-"(). * changed the default probability of arc inclusion for the "ordered" method in random.graph() to get sparser graphs. * added graphviz.plot(). * implemented the possibility of not learning arc directions in constraint-based algorithms. * changed the default value of the strict parameter from TRUE to FALSE. * reimplemented cache.structure() in C to increase random.graph() performance and scalability. bnlearn (1.0) * completely rewritten random.graph(); now it supports different generation algorithms with custom tuning parameters. * moved to dynamic memory allocation in C routines. * improved performance and error handling of rbn(). bnlearn (0.9) * reimplemented all the functions that deal with cycles and paths in C, which increased their speed manifold and greatly improved their memory use. * cycle detection and elimination snow parallelized in gs(), iamb(), fast.iamb() and inter.iamb(). * renamed {root,leaf}nodes() to {root,leaf}.nodes(). * rewritten node ordering in C to improve performance and avoid recursion. * added ci.test(), which provides a fronted to all the independence and conditional independence tests implemented in the package. * added mutual information (for Gaussian data) and Pearson's X^2 tests (for discrete data). * removed the Mantel-Haenszel test. bnlearn (0.8) * added support for random restarts in hc(). * added arc.strength(), with support for both conditional independence tests and network scores. * added the asia data set. * lots of documentation updates. * reimplemented functions related to undirected arcs in C for speed. * added more parameter sanitization. bnlearn (0.7) * optimized hc() via score caching, score equivalence, and partial reimplementation in C. * many utility functions' backends reimplemented in C for speed. * improved cycle and path detection. * lots of documentation updates. * added more parameter sanitization. bnlearn (0.6) * implemented hc(). * added support for the K2 score for discrete networks. * ported Gaussian posterior density from the deal package. * added the gaussian.test data set. * added an AIC-based test for discrete data. * lots of documentation updates. * added more parameter sanitization. bnlearn (0.5) * added more utility functions, such as rootnodes(), leafnodes(), acyclic(), empty.graph() and random.graph(). * reimplemented parents' configuration generation in C for speed. * lots of documentation updates. * added lots of parameter sanitization in utils-sanitization.R. bnlearn (0.4) * added rbn(), with support for discrete data. * added a score function, with support for likelihood, log-likelihood, AIC, BIC, and posterior Dirichlet density of discrete networks. * ported modelstring(), a string representation of a network, from package deal. * added many utility functions, such as parents() and children() and their counterparts "parents<-"() and "children<-"(). * lots of documentation updates. bnlearn (0.3) * added support for the snow package in gs(), iamb(), inter.iamb() and fast.iamb(). * added the learning.test data set. * reimplemented mutual information in C for speed. * lots of documentation updates. bnlearn (0.2) * implemented iamb(), inter.iamb() and fast.iamb(). * added partial correlation and Fisher's Z conditional independence tests for Gaussian data. * first completely documented release. bnlearn (0.1) * initial release. * preliminary implementation of gs() with mutual information as conditional independence test.