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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 cpq() 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.