Man pages for bnclassify
Learning Discrete Bayesian Network Classifiers from Data
| accuracy | Compute predictive accuracy. |
| aode | Learn an AODE ensemble. |
| are_factors | Checks if all columns in a data frame are factors. |
| are_pdists | Returns 'TRUE' is 'x' is a valid probability distribution. |
| as_mlr | Convert to 'mlr'. |
| augment_kdb | Arcs that do not invalidate the k-DB structure |
| augment_kdb_arcs | Returns augmenting arcs that do not invalidate the k-DB. |
| augment_ode | Arcs that do not invalidate the tree-like structure |
| augment_ode_arcs | Returns augmenting arcs that do not invalidate the ODE. |
| bnc | Learn network structure and parameters. |
| bnc_aode | Returns a 'c("bnc_aode", "bnc")' object. |
| bnc_aode_bns | Fits an AODE model. |
| bnc_bn | Bayesian network classifier with structure and parameters. |
| bnc_dag | Bayesian network classifier structure. |
| bnclassify | Learn discrete Bayesian network classifiers from data. |
| bootstrap_ss | Return a bootstrap sub-sample. |
| car | Car Evaluation Data Set. |
| check_mlr_attached | Checks if mlr attached. |
| cmi | Compute the (conditional) mutual information between two... |
| cmi_table | Returns the conditional mutual information three variables. |
| complete_graph | Returns a complete unweighted graph with the given nodes. |
| compute_cll | Computes the conditional log-likelihood of the model on the... |
| compute_ll | Computes log-likelihood of the model on the provided data. |
| compute_wanbia_weights | Compute WANBIA weights. Computes feature weights by... |
| cpt_vars_values | Get just form first dimension in their own cpt, not checking... |
| cv | Estimate predictive accuracy with stratified cross... |
| dag | Get underlying graph. This should be exported. |
| direct_forest | Direct an undirected graph. |
| direct_tree | Direct an undirected graph. |
| extract_ctgt | Returns a contingency table over the variables. |
| fast_equal | Compares all elements in a to b |
| forget | Forget a memoized function. |
| get_ancestors | Based on gRbase::ancestors() |
| get_but_last | Return all but last element of x. |
| get_last | Return last element of x. |
| get_log_leaf_entries | Assuming that the cpt is a leaf, returns 1 instead of a CPT... |
| get_null_safe | Get i-th element of x. |
| grain_and_graph | Convert to igraph and gRain. |
| graph_add_edges | Add edges Does not allow edges among adjacent nodes |
| graph_connected_components | connected_components |
| graph_get_adjacent | Finds adjacent nodes. Has not been tested much |
| graph_is_adjacent | Checks whether nodes are adjacent |
| graph_named_edge_matrix | Returns an edge matrix with node names (instead of node... |
| graph_subgraph | Subgraph. Only for a directed graph? |
| graph_union | Merges multiple disjoint graphs into a single one. |
| greedy_wrapper | Learn Bayesian network classifiers in a a greedy wrapper... |
| identify_all_testing_depths | Identifies all depths at which the features of a... |
| identify_min_testing_depths | Identifies the lowest (closest to root) depths at which the... |
| inspect_bnc_bn | Inspect a Bayesian network classifier (with structure and... |
| inspect_bnc_dag | Inspect a Bayesian network classifier structure. |
| is_aode | Is it en AODE? |
| is.memoised | Is it memoized? |
| learn_params | Learn the parameters of a Bayesian network structure. |
| learn_unprunned_tree | Learns a unpruned 'rpart' recursive partition. |
| local_ode_score_contrib | Returns pairwise component of ODE (penalized) log-likelihood... |
| loglik | Compute (penalized) log-likelihood. |
| log_normalize | Normalize log probabilities. |
| make_cll | Returns a function to compute negative conditional... |
| make_cll_gradient | Returns a function to compute the gradient of negative... |
| makeRLearner.bnc | makeRLearner. Auxiliary mlr function. |
| map | Assigns instances to the most likely class. |
| max_weight_forest | Returns the undirected augmenting forest. |
| memoise_char | Memoise a function. |
| nb | Learn a naive Bayes network structure. |
| nb_dag | Returns a naive Bayes structure |
| new_cache | Make a new cache. |
| order_acyclic | Provide an acyclic ordering (i.e., a topological sort). |
| plot.bnc_dag | Plot the structure. |
| predict.bnc_fit | Predicts class labels or class posterior probability... |
| predictLearner.bnc | predictLearner. Auxiliary mlr function. |
| print.bnc_base | Print basic information about a classifier. |
| skip_assert | Whether to do checks or not. Set TRUE to speed up debugging... |
| skip_testing | Skip while testing to isolate errors |
| spode | Returns a Superparent one-dependence estimator. |
| subset_by_colnames | Subset a 2D structure by a vector of column names. |
| superparent_children | Return nodes which can be superparents along with their... |
| tan_chowliu | Learns a one-dependence estimator using Chow-Liu's algorithm. |
| trainLearner.bnc | trainLearner. Auxiliary mlr function. |
| voting | Congress Voting Data Set. |
