-
Notifications
You must be signed in to change notification settings - Fork 38
/
NAMESPACE
162 lines (157 loc) · 5.84 KB
/
NAMESPACE
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
export(
# constraint-based structure learning algorithms.
"gs", "iamb", "inter.iamb", "fast.iamb", "iamb.fdr", "mmpc", "si.hiton.pc",
"pc.stable", "hpc",
# local structure learning algorithms.
"chow.liu", "aracne",
# score-based structure learning algorithms.
"hc", "tabu", "structural.em",
# hybrid structure learning algorithms.
"rsmax2", "mmhc", "h2pc",
# learning neighbours and Markov blankets.
"learn.mb", "learn.nbr",
# Bayesian network classifiers.
"naive.bayes", "tree.bayes",
# whitelists and blacklists.
"whitelist", "blacklist", "ordering2blacklist", "tiers2blacklist",
"set2blacklist",
# functions to compare network structures.
"compare", "hamming", "shd",
# get neighbours and Markov blankets.
"mb", "nbr",
# get, set and count arcs and edges; add nodes.
"arcs", "arcs<-", "directed.arcs", "undirected.arcs", "incoming.arcs",
"outgoing.arcs", "incident.arcs", "compelled.arcs", "reversible.arcs",
"narcs", "in.degree", "out.degree", "set.arc", "drop.arc", "reverse.arc",
"set.edge", "drop.edge", "add.node", "remove.node", "rename.nodes",
# get, set and count sets of nodes: parents, children, etc.
"parents", "parents<-", "children", "children<-", "spouses", "ancestors",
"descendants", "root.nodes", "leaf.nodes", "isolated.nodes", "nnodes",
# get and set adjacency matrices.
"amat", "amat<-",
# model string formulas.
"modelstring", "modelstring<-", "model2network",
# arc strength and model averaging.
"arc.strength", "boot.strength", "bf.strength", "custom.strength",
"averaged.network", "inclusion.threshold",
# networks scores and conditional independence tests.
"alpha.star", "BF", "ci.test",
# resampling and Bayesian networks.
"bn.boot", "bn.cv", "loss",
# notable network structure transforms.
"skeleton", "pdag2dag", "cpdag", "cextend", "moral", "mutilated",
# v-structures and d-separation.
"vstructs", "dsep", "colliders", "unshielded.colliders", "shielded.colliders",
# plotting network structures.
"graphviz.plot", "strength.plot", "graphviz.compare", "graphviz.chart",
# fitted Bayesian networks.
"bn.fit", "custom.fit", "bn.net", "gbn2mvnorm", "mvnorm2gbn",
# plotting fitted Bayesian networks.
"bn.fit.qqplot", "bn.fit.histogram", "bn.fit.xyplot", "bn.fit.barchart",
"bn.fit.dotplot",
# simulation facilities.
"empty.graph", "complete.graph", "random.graph", "cpdist", "rbn",
# data preprocessing and imputation.
"discretize", "dedup", "impute",
# inference.
"H", "KL", "cpquery",
# import/export functions for varous file formats.
"read.bif", "write.bif", "read.dsc", "write.dsc", "read.net", "write.net",
"write.dot",
# utility functions to manipulate test/score counters.
"test.counter", "increment.test.counter", "reset.test.counter",
# assorted functions involving network structures.
"acyclic", "directed", "path.exists", "node.ordering", "subgraph",
# assorted functions involving fitted networks.
"identifiable", "singular",
# assorted functions to extract information.
"configs", "nparams", "ntests",
# assorted conversion functions.
"as.bn", "as.bn.fit", "as.grain", "as.graphNEL", "as.graphAM", "as.igraph",
"as.lm",
# graph enumeration.
"count.graphs"
)
useDynLib(bnlearn, .registration = TRUE)
importFrom("methods", "new", "setClass", "setGeneric", "setMethod")
importFrom("stats", "logLik", "AIC", "BIC", "coefficients", "complete.cases",
"cor", "dnorm", "ecdf", "fitted", "formula", "knots", "lm", "optimize",
"quantile", "residuals", "sd", "weighted.mean", "median", "qunif", "sigma")
importFrom("grDevices", "col2rgb", "colors", "dev.cur")
importFrom("graphics", "abline", "arrows", "lines", "plot", "points",
"strheight", "strwidth", "text", "plot.new", "title", "rect", "symbols")
importFrom("parallel", "parLapplyLB", "parSapplyLB", "clusterEvalQ")
S3method(as.bn, "fit")
S3method(all.equal, "bn")
S3method(all.equal, "bn.fit")
S3method(print, "bn")
S3method(plot, "bn")
S3method(plot, "bn.fit")
S3method(AIC, "bn")
S3method(AIC, "bn.fit")
S3method(BIC, "bn")
S3method(BIC, "bn.fit")
S3method(logLik, "bn")
S3method(logLik, "bn.fit")
S3method(as.bn, "character")
S3method(as.character, "bn")
S3method(as.grain, "bn.fit")
S3method(as.bn, "grain")
S3method(as.bn.fit, "grain")
S3method(as.bn, "pcAlgo")
S3method(as.graphNEL, "bn")
S3method(as.bn, "graphNEL")
S3method(as.graphNEL, "bn.fit")
S3method(as.graphAM, "bn")
S3method(as.bn, "graphAM")
S3method(as.graphAM, "bn.fit")
S3method(as.igraph, "bn")
S3method(as.igraph, "bn.fit")
S3method(as.bn, "igraph")
S3method(as.lm, "bn")
S3method(as.lm, "bn.fit")
S3method(as.lm, "bn.fit.gnode")
S3method(print, "bn.tan")
S3method(print, "bn.fit")
S3method(print, "bn.fit.dnode")
S3method(print, "bn.fit.onode")
S3method(print, "bn.fit.gnode")
S3method(print, "bn.fit.cgnode")
S3method(residuals, "bn")
S3method(residuals, "bn.fit")
S3method(residuals, "bn.fit.dnode")
S3method(residuals, "bn.fit.onode")
S3method(residuals, "bn.fit.gnode")
S3method(residuals, "bn.fit.cgnode")
S3method(fitted, "bn")
S3method(fitted, "bn.fit")
S3method(fitted, "bn.fit.dnode")
S3method(fitted, "bn.fit.onode")
S3method(fitted, "bn.fit.gnode")
S3method(fitted, "bn.fit.cgnode")
S3method(sigma, "bn")
S3method(sigma, "bn.fit")
S3method(sigma, "bn.fit.dnode")
S3method(sigma, "bn.fit.onode")
S3method(sigma, "bn.fit.gnode")
S3method(sigma, "bn.fit.cgnode")
S3method(coef, "bn")
S3method(coef, "bn.fit")
S3method(coef, "bn.fit.dnode")
S3method(coef, "bn.fit.onode")
S3method(coef, "bn.fit.gnode")
S3method(coef, "bn.fit.cgnode")
S3method(print, "bn.kcv")
S3method(plot, "bn.kcv")
S3method(print, "bn.kcv.list")
S3method(plot, "bn.kcv.list")
S3method(plot, "bn.strength")
S3method(predict, "bn.fit")
S3method(predict, "bn.naive")
S3method(predict, "bn.tan")
S3method("$<-", "bn.fit")
S3method("[[<-", "bn.fit")
S3method(mean, "bn.strength")
S3method(mean, "bn.fit")
exportClasses("bn", "bn.fit")
exportMethods("nodes", "nodes<-", "degree", "score")