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wip/Rbnlearn,
Bayesian network structure learning, parameter learning and inference
Branch: CURRENT,
Version: 4.0,
Package name: Rbnlearn4.0,
Maintainer: mishkaBayesian network structure learning, parameter learning and inference.
This package implements constraintbased (GS, IAMB, InterIAMB,
FastIAMB, MMPC, HitonPC), pairwise (ARACNE and ChowLiu),
scorebased (HillClimbing 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 TreeAugmented
Naive Bayes (TAN) classifiers are also implemented.
Some utility functions (model comparison and manipulation, random
data generation, arc orientation testing, simple and advanced plots)
are included, as well as support for parameter estimation (maximum
likelihood and Bayesian) and inference, conditional probability
queries and crossvalidation.
Required to run:[
math/R] [
math/blas]
Master sites: (Expand)
Version history: (Expand)
 (20171123) Package has been reborn
 (20170502) Package has been reborn
 (20170501) Package deleted from pkgsrc
 (20160921) Updated to version: Rbnlearn4.0
 (20160123) Package has been reborn
 (20160121) Package deleted from pkgsrc
CVS history: (Expand)
20150302 15:31:25 by Mike M. Volokhov  Files touched by this commit (3)  
Log message:
Update Rbnlearn 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 constraintbased 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 JeanBaptiste
Denis).
* implemented posterior predictions in predict() using likelihood weighting.
* prevent silent reuse of AIC penalization coefficient when computing BIC
and vice versa (thanks MarÐÂa 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 multiplevalued evidence for likelihood weighting
in cp{query,dist}().
* implemented dedup() to preprocess 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().

20131009 16:20:22 by Mike M. Volokhov  Files touched by this commit (2)  
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 JeanBaptiste Denis).
* revised optimizations in constraintbased algorithms, removing most
false positives by sacrificing speed.
* fixed warning in cp{dist,query}().
* added support for ordered factors.
* implemented the JonckheereTerpstra test to support ordered factors
in constraintbased 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}().

20130329 14:12:00 by Mike M. Volokhov  Files touched by this commit (2)  
Log message:
Update Rbnlearn 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 JeanBaptiste 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 JeanBaptiste Denis).
* check vstructures do not introduce directed cycles in the
graph when applying them (thanks JeanBaptiste Denis).
* fixed a buffer overflow in cextend() (thanks JeanBaptiste
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).

20130319 02:22:55 by Mike M. Volokhov  Files touched by this commit (17) 
Log message:
Move LICENSE right below COMMENT, that's where it usually should be.
Noted by <wiz>  thank you very much!

20121116 02:05:42 by Mike M. Volokhov  Files touched by this commit (3)  
Log message:
Update Rbnlearn 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 vstructure.
* 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.

20120830 21:03:42 by Mike M. Volokhov  Files touched by this commit (10) 
Log message:
Cleanup Makefiles and get them ready to import.

20120829 23:40:37 by Mike M. Volokhov  Files touched by this commit (1) 
Log message:
Author provides a perfect overview in the package description. Sync to it.

20120828 17:39:17 by Mike M. Volokhov  Files touched by this commit (6) 
Log message:
Fix spacing.
