bnlearn (5.0) * completed the implementation of KL(), which now supports conditional Gaussian networks in addition to discrete and Gaussian ones. * implemented Shannon's entropy. * conditional independence tests are now have optional arguments like network scores. * added a "custom-test" conditional independence test allowing user-provided test statistics in the same way as "custom" allows user-provided network scores. * added a "params.threshold" to hard EM methods in bn.fit(), and renamed the log-likelihood threshold to "loglik.threshold". * the log-likelihood stopping rule in hard EM now uses the log-likelihood of the completed data, which works better in the presence of latent variables and is more appropriate accoridng to Koller & Friedman (thanks Laura Azzimonti). bnlearn (4.9.1) * assorted fixes to the Rprintf() format strings to pass the CRAN tests. * the default node shape in graphviz.plot(), strength.plot() and graphviz.compare() is now "rectangle", which is more space-efficient for typical node labels. * graphviz.compare() now accepts bn.fit objects, converting them to the corresponding bn objects to compare the respective network structures. * fixed a segfault in ci.test(), triggered by setting the conditioning variable set to a zero-column matrix (thanks Qingyuan Zheng). bnlearn (4.9) * as.prediction() is now deprecated and will be removed by the end of 2024. * graphviz.plot(), strength.plot() and graphviz.compare() now have a "fontsize" argument that controls the font size of the node labels. * removed the rbn() method for bn objects. * predict() and impute() can now use exact inference with method = "exact" for discrete and Gaussian networks. * the "custom" score now accepts incomplete data. * it is now possible to use the "custom" score to implement custom Bayesian scores in BF() and bf.strength(). * fixed the conditional probabilities computed by cpquery(), which now disregards particles for which either the evidence or the event expressions evaluate to NA (thanks Simon Rauch). * added a complete.graph() function to complement empty.graph(). * removed the "empty" method of random.graph(), use empty.graph() instead. * structural.em() can now use exact inference in the expectation step with impute = "exact". * structural.em() can now be called from bn.boot(), boot.strength() and bn.cv(). * updated as.bn.fit() to work with the latest gRain release. * fixed segfault in tree.bayes() with illegal whitelists and blacklists. * predict(method = "bayes-lw") and predict(method = "exact") now work even when from = character(0). * implemented hard EM in bn.fit() with method = "hard-em" (discrete BNs), method = "hard-em-g" (Gaussian BNs) and method = "hard-em-cg" (conditional Gaussian BNs). * predict() and impute() can use clusters from the parallel package with all available methods. * hard EM methods in bn.fit() can use clusters from the parallel package like previously available parameter estimators. * boot.strength() now shuffles the columns of the data by default, which seems to broadly improve structural accuracy. * constraint-based algorithms are now guaranteed to return a CPDAG; this was not the case previously because shielded colliders were preserved along with unshielded ones (thanks Ruben Camilo Wisskott). * bn.fit() now works with network classifiers (thanks Riley Mulhern). * logLik() has been re-implemented and now accepts incomplete data. * tabu search now works with continuous data containing latent variables (thanks David Purves). * read.net() can now parse interval nodes (thanks Marco Valtorta). * graphviz.chart() now handles text.col correctly even when it contains a separate colour for each node. * impute() now produces an error instead of returning data still containing missing values (with "strict" set to TRUE, the default) or at least it produces a warning (with "strict" set to FALSE). * better sanitization of CPTs in custom.fit() (thanks Dave Costello). * implemented the node-average (penalized) likelihood scores from Bodewes and Scutari for discrete ("nal" and "pnal"), Gaussian ("nal-g" and "pnal-g") and conditional Gaussian ("nal-cg" and "pnal-cg") BNs. * predict() for bn.fit objects now accepts incomplete data, conditioning on the observed values and averaging over the missing values in each observations in the case of method = "bayes-lw" and method = "exact". bnlearn (4.8.1) * assorted fixes to the C code to pass the CRAN tests. bnlearn (4.8) * the rbn() method for bn objects is now deprecated and will be removed by the end of 2023. * removed choose.direction(). * implemented gbn2mvnorm(), which converts a Gaussian BN to its multivariate normal global distribution, and mvnorm2gbn(), which does the opposite. * added the extended BIC from Foygel and Drton. * the maximum likelihood estimators in bn.fit() now have distinct labels "mle" (discrete BNs), "mle-g" (Gaussian BNs) and "mle-cg" (conditional Gaussian BNs) to identify them without ambiguity. * graphviz.chart() now supports Gaussian and conditional Gaussian BNs (thanks Tom Waddell). * the "draw.levels" argument of graphviz.chart() has been renamed to "draw.labels". * chow.liu(), aracne() and tree.bayes() now handle data with missing values. * implemented the hierarchical Dirichlet parameter estimator for related data sets from Azzimonti, Corani and Zaffalon. * all unidentifiable parameters are now NAs, including those that were NaNs before, for consistency across node types. * structural.em() now returns descriptive error messages when the data contain latent variables (thanks Bernard Liew). * implemented the Kullback-Leibler divergence for discrete and Gaussian networks. * fixed spurious errors in bn.boot() resulting from changes in the all.equal() method for functions in R 4.1.0 (thanks Fred Gruber). * added a mean() method that averages bn.fit objects with the same network structure, with optional weights. * bn.boot(), bn.cv() and boot.strength() now handle data with missing values. bnlearn (4.7) * removed the "moral" argument from vstructs() and cpdag(). * removed the path() alias to path.exists(). * the "nodes" argument has been removed from averaged.network(), it was not meaningfully used anywhere. * the choose.direction() function is now deprecated, and it will also be removed by the end of 2022. * faster sanitization of bn objects (thanks David Quesada). * fixed an overflow in the BGe score that produced NaN values (thanks David Quesada). * Date and POSIXct objects are not valid inputs for functions in bnlearn (thanks David Purves). * export the function computing the significance threshold in averaged.network() as inclusion.threshold() (thanks Noriaki Sato). * fixed the sanitization of custom cutpoints in strength.plot(). * reimplemented discretize() in C for speed, Hartemink's discretization is faster by a factor of at least 2x. * discretize() now handles data with missing values. * merged an implementation of the factorized NML and the quotient NML scores from Tomi Silander. bnlearn (4.6.1) * Fixed out-of-bounds memory access in discretize() (thanks Brian Ripley). bnlearn (4.6) * removed support for parametric bootstrap in bn.boot(). * path() has been renamed path.exists(); path() will be kept as an alias until 2021 when it will be removed to avoid clashing with BiocGenerics. * the "moral" arguments of vstructs() and cpdag() are now deprecated and it will be removed in 2021. * fixed graphviz.chart(), which called plot.new() unnecessarily and created empty figures when a graphical device such as pdf() was already open. * added a "custom" (decomposable) score that takes a user-specified R function to compute the score of local distributions in score() and structure learning algorithms (thanks Laura Azzimonti). * fixed spouses(), which now always returns a character vector. * added an "including.evidence" argument to the as.bn.fit() method for grain objects to carry over hard evidence in the conversion (thanks Rafal Urbaniak). * bn.fit() with missing data is now faster by 2x-3x. * due to API changes, bnlearn now suggests gRain >= 1.3-3. * fixed permutation tests, which incorrectly used a strict inequality when computing the fraction of test statistics larger than that computed from the original data (thanks David Purves). * make cpdag() and vstructs() agree for both moral = FALSE and moral = TRUE (thanks Bingling Wang). * implemented colliders(), shielded.colliders() and unshielded.colliders(); vstructs() is now an alias of unshielded.colliders(). * added functions to import and export igraph objects. * fixed pc.stable(), which failed on two-variables data sets. * added utility functions set2blacklist(), add.node(), remove.node(), rename.nodes(). * fixed h2pc(), which failed when encountering isolated nodes (thanks Kunal Dang). * better argument sanitization for threshold and cutpoints in strength.plot(). * fixed "newdata" argument sanitization for the pred-loglik-* scores. * read.net() now disregards experience tables instead of generating an error when importing NET files from Hugin (thanks Jirka Vomlel). * fixed bug in mmpc(), which did return wrong maximum/minimum p-values (thanks Jireh Huang). bnlearn (4.5) * the "parametric" option for the "sim" argument of bn.boot() is now deprecated; the argument will be removed in 2020. * removed the relevant() function, and the "strict" and "optimized" arguments of constraint-based structure learning algorithms. * save arc strengths as weights in the graph object returned by strength.plot() (thanks Fabio Gori). * information about illegal arcs in now preserved in averaged.network(), so that cpdag() works correctly on the returned network. * loss function "pred" (classification error, predicted values from parents) is now distinct from "pred-exact" (classification error, exact posterior predicted values for classifiers); and it is now possible to use "pred" and "pred-lw" in bn.cv() when the model is a BN classifier (thanks Kostas Oikonomou). * graphviz.compare() now returns a list containing the graph objects corresponding to the networks provided as arguments (thanks William Raynor). * the "from-first" method in graphviz.compare() now has a "show.first" argument that controls whether the reference network is plotted at all (thanks William Raynor). * implemented the IAMB-FDR, HPC and H2PC structure learning algorithms. * reimplemented the BGe score using the updated unbiased estimator from Kuipers, Moffa and Heckerman (2014). * fixed the test counter in constraint-based algorithms, which would overcount in some cases. * it is now possible to use any structure learning algorithm in bn.boot() and bn.cv(). * fixed prediction from parents for conditional Gaussian nodes with no continuous parents (thanks Harsha Kokel). * it is now possible to use data with missing values in learn.mb(), learn.nbr() and in all constraint-based structure learning algorithms. * fixed tabu() in the presence of zero-variance continuous variables; the search was not correctly initialized because the starting model is singular (thanks Luise Gootjes-Dreesbach). * implemented predictive log-likelihood scores for discrete, Gaussian and conditional Gaussian networks. * fixed an integer overflow in the nparams() method for bn.fit objects (thanks Yujian Liu). * make conditional sampling faster for large conditional probability tables (thanks Yujian Liu). * preserve structure learning information in bn.cv(), so that custom.strength() can get directions right from the resulting set of networks (thanks Xiang Liu). * revised the preprocessing of whitelists and blacklists, and clarified the documentation (thanks Michail Tsagris). * added a "for.parents" argument to coef() and sigma() to make them return the parameters associated with a specific configuration of the discrete parents of a node in a bn.fit object (thanks Harsha Kokel). * fixed segfault in predict(..., method = "bayes-lw") from data that contain extra variables that are not in the network (thanks Oliver Perkins). bnlearn (4.4) * fixed pc.stable() v-structure detection in the presence of blacklisted arcs. * warn about trying to cextend() networks that contain no information about arc directions (and thus v-structures), such as those learned with "undirected = TRUE" or those returned by skeleton(). * fixed a bug because of which a number of functions incorrectly reported that data had variables with no observed values when that was not true. * fixed posterior imputation from a single observed variable (thanks Derek Powell). * added an argument "max.sx" to limit the maximum allowed size of the conditioning sets in the conditional independence tests used in constraint-based algorithms and in learn.{mb,nbr}(). * do not generate an error when it is impossible to compute a partial correlation because the covariance matrix cannot be (pseudo)inverted; generate a warning and return a zero partial correlation instead. * added an as.lm() function to convert Gaussian networks and nodes to (lists of) lm objects (thanks William Arnost). * fixed the penalty terms of BIC and AIC, which did not count residual standard errors when tallying the parameters of Gaussian and conditional Gaussian nodes. * cpdag() failed to set the directions of some compelled arcs when both end-nodes have parents (thanks Topi Talvitie). * custom.strength() now accepts bn.fit objects in addition to bn objects and arc sets. * vstructs() mistakenly handled moral = TRUE as if it were moral = FALSE (thanks Christian Schuhegger). * graphviz.plot() and strength.plot() now have a "render" argument that controls whether a figure is produced (a graph object is always returned from both functions). * graphviz.plot(), strength.plot() and graphviz.compare() now have a "groups" argument that specifies subsets of nodes that should be plotted close to each other, layout permitting. * fixed tree.bayes() for data frames with 2-3 variables, and chow.liu() as well (thanks Kostas Oikonomou). bnlearn (4.3) * the "strict" and "optimized" arguments of constraint-based algorithms are now deprecated and will be removed at the beginning of 2019. * the relevant() function is now deprecated, and it will also be removed at the beginning of 2019. * improved and fixed a few bugs in the functions that import and export bn and bn.fit objects to the graph package. * fixed a bug in averaged.network(), which could result in inconsistent bn objects when arcs were dropped to obtain an acyclic graph (thanks Shuonan Chen). * added a graphviz.chart() function to produce DAG-with-barchart-nodes plots. * fixed the counting of the number of parameters of continuous and hybrid networks, which did not take the residual standard errors into account (thanks Jeffrey Hart). * improved handling of singular models in impute(). * added an import function for pcAlgo objects from pcalg. * fixed bug in the sanitization of conditional Gaussian networks (thanks Kostas Oikonomou). * added a loss() function to extract the estimated loss values from the objects returned by bn.cv() (thanks Dejan Neskovic). * it is now possible to use data with missing values in bn.fit() and nparams(). * added a "replace.unidentifiable" argument to bn.fit(..., method = "mle"), to replace parameter estimates that are NA/NaN with zeroes (for regression coefficients) and uniform probabilities (in conditional probability tables). * added a bf.strength() function to compute arc strengths using Bayes factors. * learn.{mb,nbr}() now work even if all nodes are blacklisted. * assigning singular models from lm() to nodes in a bn.fit object will now zapsmall() near-zero coefficients, standard errors and residuals to match the estimates produced by bn.fit(). * bn.cv() now supports performing multiple runs with custom folds (different for each run). * improved sanitization in mutilated(), and updated its documentation. * removed the bibTeX file with references, available at www.bnlearn.com. * implemented the stable version of the PC algorithm. * added a count.graph() function that implements a number of graph enumeration results useful for studying graphical priors. * fixed loss estimation in bn.cv() for non-extendable partially directed graphs, now errors are produced instead of returning meaningless results (thanks Derek Powell). bnlearn (4.2) * added a tuning parameter for the inclusion probability to the marginal uniform graph prior. * added a Bayesian Dirichlet score using Jeffrey's prior (from Joe Suzuki). * allow fractional imaginary sample sizes for posterior scores. * allow imaginary sample sizes in (0, 1] for discrete posterior scores, to explore asymptotic results. * set the default imaginary sample size for discrete networks to 1, following recommendations from the literature. * moral(), cpdag(), skeleton() and vstructs() now accept bn.fit objects in addition to bn objects. * fixed a segfault in cpdist(..., method = "lw") caused by all weights being equal to NaN (thanks David Chen). * changed the default value of the "optimized" argument to "FALSE" in constraint-based algorithms. * changed the arguments of mmhc() and rsmax2() to improve their flexibility and to allow separate "optimized" values for the restrict and maximize phases. * fixed sanitization of fitted networks containing ordinal discrete variables (thanks David Chen). * improved argument sanitization in custom.fit() and model string functions. * added a BF() function to compute Bayes factors. * added a graphviz.compare() function to visually compare network structures. * implemented the locally averaged Bayesian Dirichlet score. * custom.strength() now accepts bn.kcv and bn.kcv objects and computes arc strengths from the networks learned by bn.cv() in the context of cross-validation. * fixed multiple bugs in cextend() and cpdag() that could result in the creation of additional v-structures. * implemented the Structural EM algorithm in structural.em(). * fixed multiple bugs triggered by missing values in predict() (thanks Oussama Bouldjedri). * implemented an as.prediction() function that exports objects of class bn.strength to the ROCR package (contributed by Robert Ness). bnlearn (4.1) * fixed memory corruption in dsep() (thanks Dominik Muller). * added the marginal uniform prior. * fixed the optimized score cache for the Castelo & Siebes and for the marginal uniform priors, which were affected by several subtle bugs. * bn.cv() now implements a "custom-folds" method that allows to manually specify which observation belongs to each fold, and folds are not constrained to have the same size. * fixed checks in the C code involving R objects' classes; they failed when additional, optional classes were present (thanks Claudia Vitolo). * fixed cpdag() handling of illegal arcs that are part of shielded colliders (thanks Vladimir Manewitsch). * removed misleading warning about conflicting v-structures from cpdag(). * rsmax2() and mmhc() now return whitelists and blacklists as they are at the beginning restrict phase (thanks Vladimir Manewitsch). * bn.fit() can now fit local distributions in parallel, and has been mostly reimplemented in C for speed (thanks Claudia Vitolo). * added an impute() function to impute missing values from a bn.fit object. * fixed loss functions for data in which observations have to be dropped for various nodes (thanks Manuel Gomez Olmedo). * added an all.equal() method to compare bn.fit objects. * added a "by.node" argument to score() for decomposable scores (thanks Behjati Shahab). * added warning about partially direct graphs in choose.direction() and improved its debugging output (thanks Wei Kong). * added spouses(), ancestors() and descendats(). * fixed a segfault in predict(..., method = "lw") with discrete BNs and sparse CPTs that included NaNs. bnlearn (4.0) * fixed memory usage in aracne(), chow.liu() and tree.bayes() (thanks Sunkyung Kim). * rework memory management using calloc() and free() to avoid memory leaks arising from R_alloc() and missing memory barriers. * fixed a coefficients indexing bug in rbn() for conditional Gaussian nodes (thanks Vladimir Manewitsch). * added a mean() function to average bn.strength objects. * fixed S4 method creation on package load on MacOS X (thanks Dietmar Janetzko) * fixed more corner cases in the Castelo & Siebes prior, and increased numeric tolerance for prior probabilities. * allow non-uniform priors for the "mbde" score (thanks Robert Ness) and for "bdes". * the "mode" attribute in bn.strength objects it now named "method". * added posterior probabilities to the predictions for all discrete networks (thanks ShangKun Deng). * added the Steck's optimal ISS estimator for the BDe(u) score. * fixed the assignment of standard deviation in fitted CLG networks (thanks Rahul Swaminathan). * handle zero lambdas in the shrinkage Gaussian mutual information (thanks Piet Jones). * fixed segfault when computing posterior predictions from networks with NaNs in their conditional probability tables (thanks Giulio Caravagna). * fixed the assignment of LASSO models from the penalized package to fitted Gaussian networks (thanks Anthony Gualandri). * cpdag() now preserves the directions of arcs between continuous and discrete nodes in conditional linear Gaussian networks, and optionally also takes whitelists and blacklist into account (for any network). * several checks are now in place to prevent the inclusion of illegal arcs in conditional Gaussian networks. * renamed the "ignore.cycles" argument to "check.cycles" in arcs<-() and amat<-() for consistency with other functions such as set.arc(). * added an "undirected" argument to mmpc() and si.hiton.pc(), which can now learn the CPDAG of the network instead of just the skeleton. * added a "directed" argument to acyclic(). * removed unsupported argument "start" from learn.nbr(). * handle interventions correctly in boot.strength() when using the mixed BDe score (thanks Petros Boutselis). * "bdes" is now named "bds" (it is not score equivalent, so the "e" did not belong). bnlearn (3.9) * fixed alpha threshold truncation bug in conditional independence tests (thanks Janko Tackmann). * massive cleanup of the C code handling conditional independence tests. * fixed variance scaling bug for the mi-cg test (thanks Nicholas Mitsakakis). * in the exact t-test for correlation and in Fisher's Z, assume independence instead of returning an error when degrees of freedom are < 1. * fixed segfault in cpdist(..., method = "lw") when the evidence has probability zero. * added loss functions based on MAP predictions in bn.cv(). * removed bn.moments() and bn.var(), they were basically unmaintained and had numerical stability problems. * added support for hold-out cross-validation in bn.cv(). * added plot() methods for comparing the results of different bn.cv() calls. * permutation tests should return a p-value of 1 when one of the two variables being tested is constant (thanks Maxime Gasse). * improved handling of zero prior probabilities for arcs in the Castelo & Siebes prior, so that hc() and tabu() do not get stuck (thanks Jim Metz). * added an "effective" argument to compute the effective degrees of freedoms of the network, estimated with the number of non-zero free parameters. * fixed optional argument handling in rsmax2(). * fixed more corner cases related to singular models in cpdist(..., method = "lw") and predict(..., method = "bayes-lw"). * fixed Pearson's X^2 test, zero cells may have dropped too often in sparse contingency tables. * fixed floating point rounding issues in the shrinkage estimator for the Gaussian mutual information. bnlearn (3.8.1) * fixed CPT import in read.net(). * fixed penfit objects import from penalized (thanks John Noble). * fixed memory allocation corner case in BDe. bnlearn (3.8) * reorder CPT dimensions as needed in custom.fit() (thanks Zheng Zhu). * fixed two uninitialized-memory bugs found by valgrind, one in predict() and one random.graph(). * fixed wrong check for cluster objects (thanks Vladimir Manewitsch). * fixed the description of the alternative hypothesis for the Jonckheere-Terpstra test. * allow undirected cycles in whitelists for structure learning algorithms and let the algorithm learn arc directions (thanks Vladimir Manewitsch). * include sanitized whitelists (as opposed to those provided by the user) in bn.fit objects. * removed predict() methods for single-node objects, use the method for bn.fit objects instead. * various fixes in the monolithic C test functions. * fixed indexing bug in compare() (thanks Vladimir Manewitsch). * fixed false positives in cycle detection when adding edges to a graph (thanks Vladimir Manewitsch). * fixed prior handling in predict() for naive Bayes and TAN classifiers (thanks Vinay Bhat). * added configs() to construct configurations of discrete variables. * added sigma() to extract standard errors from bn.fit objects. bnlearn (3.7.1) * small changes to make CRAN checks 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 MarĂa 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(). bnlearn (3.5) * fixed (again) function name collisions with the graph packages (thanks Carsten Krueger). * fixed some variable indexing issues in likelihood weighting. * removed bootstrap support from arc.strength(), use boot.strength() instead. * added set.edge() and drop.edge() to work with undirected arcs. * boot.strength() now has a parallelized implementation. * added support for non-uniform graph priors (Bayesian variable selection, Castelo & Siebes). * added a threshold for the maximum number of parents in hc() and tabu(). * changed the default value of "moral" from FALSE to TRUE in cpdag() and vstructs() to ensure sensible results in model averaging. * added more sanity checks in cp{query,dist}() expression parsing (thanks Ofer Mendelevitch). * added 'nodes' and 'by.sample' arguments to logLik() for bn.fit objects. * support {naive,tree}.bayes() in bn.cv() (thanks Xin Zhou). * fixed predict() for ordinal networks (thanks Vitalie Spinu). * fixed zero variance handling in unconditional Jonckheere-Terpstra tests due to empty rows/columns (thanks Vitalie Spinu). * in bn.cv(), the default loss for classifiers is now classification error. * added a nodes<-() function to re-label nodes in bn and bn.fit object (based on a proof of concept by Vitalie Spinu). * replaced all calls to LENGTH() with length() in C code (thanks Brian Ripley). * default to an improper flat prior in predict() for classifiers for consistency (thanks Xin Zhou). * suggest the parallel package instead of snow (which still works fine). bnlearn (3.4) * move the test counter into bnlearn's namespace. * include Tsamardinos' optimizations in mmpc(..., optimized = FALSE), but not backtracking, to make it comparable with other learning algorithms. * check whether the residuals and the fitted values are present before trying to plot a bn.fit{,.gnode} object. * fixed two integer overflows in factors' levels and degrees of freedom in large networks. * added {compelled,reversible}.arcs(). * added the MSE and predictive correlation loss functions to bn.cv(). * use the unbiased estimate of residual variance to compute the standard error in bn.fit(..., method = "mle") (thanks Jean-Baptiste Denis). * revised optimizations in constraint-based algorithms, removing most false positives by sacrificing speed. * fixed warning in cp{dist,query}(). * added support for ordered factors. * implemented the Jonckheere-Terpstra test to support ordered factors in constraint-based structure learning. * added a plot() method for bn.strength objects containing bootstrapped confidence estimates; it prints their ECDF and the estimated significance threshold. * fixed dimension reduction in cpdist(). * reimplemented Gaussian rbn() in C, it's now twice as fast. * improve precision and robustness of (partial) correlations. * remove the old network scripts for network that are now available from www.bnlearn.com/bnrepository. * implemented likelihood weighting in cp{dist,query}(). bnlearn (3.3) * fixed cpdag() and cextend(), which returned an error about the input graph being cyclic when it included the CPDAG of a shielded collider (thanks Jean-Baptiste Denis). * do not generate observations from redundant variables (those not in the upper closure of event and evidence) in cpdag() and cpquery(). * added Pena's relevant() nodes identification. * make custom.fit() robust against floating point errors (thanks Jean-Baptiste Denis). * check v-structures do not introduce directed cycles in the graph when applying them (thanks Jean-Baptiste Denis). * fixed a buffer overflow in cextend() (thanks Jean-Baptiste Denis). * added a "strict" argument to cextend(). * removed Depends on the graph package, which is in Suggests once more. * prefer the parallel package to snow, if it is available. * replace NaNs in bn.fit objects with uniform conditional probabilities when calling as.grain(), with a warning instead of an error. * remove reserved characters from levels in write.{dsc,bif,net}(). * fix the Gaussian mutual information test (thanks Alex Lenkoski). bnlearn (3.2) * fixed outstanding typo affecting the sequential Monte Carlo implementation of Pearson's X^2 (thanks Maxime Gasse). * switch from Margaritis' set of rules to the more standard Meek/Sprites set of rules, which are implemented in cpdag(). Now the networks returned by constraint-based algorithms are guaranteed to be CPDAGs, which was not necessarily the case until now. * semiparametric tests now default to 100 permutations, not 5000. * make a local copy of rcont2() to make bnlearn compatible with both older and newer R versions. bnlearn (3.1) * fixed all.equal(), it did not work as expected on networks that were identical save for the order of nodes or arcs. * added a "moral" argument to cpdag() and vstructs() to make those functions follow the different definitions of v-structure. * added support for graphs with 1 and 2 nodes. * fixed cpquery() handling of TRUE (this time for real). * handle more corner cases in dsep(). * added a BIC method for bn and bn.fit objects. * added the semiparametric tests from Tsamardinos & Borboudakis (thanks Maxime Gasse). * added posterior probabilities to the predictions for {naive,tree}.bayes() models. * fixed buffer overflow in rbn() for discrete data.