Learning Discrete Bayesian Network Classifiers from Data
Compute predictive accuracy.
Learn an AODE ensemble.
Checks if all columns in a data frame are factors.
Returns TRUE is x is a valid probability distribution.
Convert to mlr.
Arcs that do not invalidate the k-DB structure
Returns augmenting arcs that do not invalidate the k-DB.
Arcs that do not invalidate the tree-like structure
Returns augmenting arcs that do not invalidate the ODE.
Learn network structure and parameters.
Returns a c("bnc_aode", "bnc") object.
Fits an AODE model.
Bayesian network classifier with structure and parameters.
Bayesian network classifier structure.
Learn discrete Bayesian network classifiers from data.
Return a bootstrap sub-sample.
Checks if mlr attached.
Compute the (conditional) mutual information between two variables.
Returns the conditional mutual information three variables.
Returns a complete unweighted graph with the given nodes.
Computes the conditional log-likelihood of the model on the provided d...
Computes log-likelihood of the model on the provided data.
Compute WANBIA weights.Computes feature weights by optimizing conditio...
Get just form first dimension in their own cpt, not checking for consi...
Estimate predictive accuracy with stratified cross validation.
Get underlying graph. This should be exported.
Direct an undirected graph.
Direct an undirected graph.
Returns a contingency table over the variables.
Compares all elements in a to b
Forget a memoized function.
Based on gRbase::ancestors()
Return all but last element of x.
Return last element of x.
Assuming that the cpt is a leaf, returns 1 instead of a CPT entry when...
Get i-th element of x.
Convert to igraph and gRain.
Add edges Does not allow edges among adjacent nodes
connected_components
Finds adjacent nodes. Has not been tested much
Checks whether nodes are adjacent
Returns an edge matrix with node names (instead of node indices).
Subgraph. Only for a directed graph?
Merges multiple disjoint graphs into a single one.
Learn Bayesian network classifiers in a a greedy wrapper fashion.
Identifies all depths at which the features of a classification tree a...
Identifies the lowest (closest to root) depths at which the features o...
Inspect a Bayesian network classifier (with structure and parameters).
Inspect a Bayesian network classifier structure.
Is it memoized?
Is it en AODE?
Learn the parameters of a Bayesian network structure.
Learns a unpruned rpart recursive partition.
Returns pairwise component of ODE (penalized) log-likelihood scores. I...
Normalize log probabilities.
Compute (penalized) log-likelihood.
Returns a function to compute negative conditional log-likelihood give...
Returns a function to compute the gradient of negative conditional log...
makeRLearner. Auxiliary mlr function.
Assigns instances to the most likely class.
Returns the undirected augmenting forest.
Memoise a function.
Learn a naive Bayes network structure.
Returns a naive Bayes structure
Make a new cache.
Provide an acyclic ordering (i.e., a topological sort).
Plot the structure.
Predicts class labels or class posterior probability distributions.
predictLearner. Auxiliary mlr function.
Print basic information about a classifier.
Whether to do checks or not. Set TRUE to speed up debugging or buildin...
Skip while testing to isolate errors
Returns a Superparent one-dependence estimator.
Subset a 2D structure by a vector of column names.
Return nodes which can be superparents along with their possible child...
Learns a one-dependence estimator using Chow-Liu's algorithm.
trainLearner. Auxiliary mlr function.
State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.
Useful links