Log message:
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().

Log message:
Update bnlearn to version 3.4. Major changes:

  * 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}().

Log message:
Update R-bnlearn to version 3.3.  Major changes:

  * 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).

Log message:
Move LICENSE right below COMMENT, that's where it usually should be.
Noted by <wiz> - thank you very much!

Log message:
Update R-bnlearn to version 3.1. Major changes:

* fixed all.equal(), it did not work as expected on networks
  that were idetical 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.

Log message:
Cleanup Makefiles and get them ready to import.

Log message:
Author provides a perfect overview in the package description. Sync to it.

Log message:
Fix spacing.