Movatterモバイル変換

Title:	Causal Graph Interface
Version:	0.3.1
Description:	Create, query, and modify causal graphs. 'caugi' (Causal Graph Interface) is a causality-first, high performance graph package that provides a simple interface to build, structure, and examine causal relationships.
License:	MIT + file LICENSE
URL:	https://frederikfabriciusbjerre.github.io/caugi/
BugReports:	https://github.com/frederikfabriciusbjerre/caugi/issues
Depends:	R (≥ 4.2)
Imports:	data.table, fastmap, S7, stats, methods
Suggests:	bnlearn, dagitty, devtools, ggm, graph, gRbase, igraph,knitr, MASS, Matrix, rextendr, rmarkdown, testthat
VignetteBuilder:	knitr
Config/rextendr/version:	0.4.2
Encoding:	UTF-8
RoxygenNote:	7.3.3
SystemRequirements:	Cargo (Rust's package manager), rustc >= 1.80.0, xz
Config/Needs/website:	rmarkdown
NeedsCompilation:	yes
Packaged:	2025-11-28 21:17:22 UTC; fabben
Author:	Frederik Fabricius-Bjerre [aut, cre, cph], Johan Larsson [aut], Michael Sachs [aut]
Maintainer:	Frederik Fabricius-Bjerre <frederik@fabriciusbjerre.dk>
Repository:	CRAN
Date/Publication:	2025-12-04 12:00:02 UTC

caugi (Causal Graph Interface)

Description

Create, query, and modify causal graphs. 'caugi' (Causal Graph Interface) isa causality-first, high performance graph package that provides a simpleinterface to build, structure, and examine causal relationships.

Author(s)

Maintainer: Frederik Fabricius-Bjerrefrederik@fabriciusbjerre.dk [copyright holder]

Authors:

• Johan Larssonjohan@jolars.co (ORCID)

• Michael Sachssachsmc@gmail.com (ORCID)

See Also

Useful links:

• https://frederikfabriciusbjerre.github.io/caugi/

• Report bugs athttps://github.com/frederikfabriciusbjerre/caugi/issues

Fit items on a line

Description

Helper function to determine how many items can fit on a lineof given width, considering an indent.

Usage

.caugi_fit_on_line(items, width, indent)

Arguments

Value

An integer indicating how many items fit on the line.

Create the state environment for acaugi (internal)

Description

Internal function to create the state environment for acaugi. This function is not intended to be used directly by users.

Usage

.cg_state(  nodes,  edges,  ptr,  built,  simple,  class,  name_index_map,  index_name_map)

Arguments

Value

An environment containing the graph state.

Collect edges and nodes

Description

Collect edges (via .parse_edge_arg) and explicitly declarednodes (no edges).

Usage

.collect_edges_nodes(calls)

Arguments

Value

A list with two elements:

• edges: adata.table with columnsfrom,edge,to

• declared: a character vector of explicitly declared nodes

Combine terms with '+'

Description

Combine a list of terms into a single left-associative '+' call.

Usage

.combine_plus(terms)

Arguments

Value

A single expression combining the terms with '+'.

Does the expression contain an edge call?

Description

Recursively check if the expression contains any edge call.

Usage

.contains_edge(expr)

Arguments

Value

TRUE if the expression contains an edge call, FALSE otherwise

Edge constructor

Description

Internal function to construct edges forcaugi objects.

Usage

.edge_constructor(from = character(), edge = character(), to = character())

Arguments

Value

Adata.table object with columnsfrom,edge, andto.

Edge constructor using indices.

Description

Internal function to construct edges forcaugi objects using indices.

Usage

.edge_constructor_idx(from_idx, edge, to_idx, node_names)

Arguments

Value

Adata.table object with columnsfrom,edge, andto.

Get edge operators

Description

This function gets the default caugi edge operators

Usage

.edge_ops_get()

Value

The current edge operators of the caugi environment

Edge specification infix operators

Description

These infix operators are used to specify edges incaugi(). This function helps build infix operators.

Usage

.edge_spec(from_sym, to_expr, glyph)

Arguments

Value

Adata.table with columnsfrom,to, andedge.

Turn edge units into adata.table of edges

Description

Convert a list of edge units into adata.table with columnsfrom,edge, andto.

Usage

.edge_units_to_dt(units)

Arguments

Value

Adata.table with columnsfrom,edge, andto.

Expand node expressions

Description

Expand node expressions: symbol, "B", c(...), +, (...)

Usage

.expand_nodes(expr, env = parent.frame())

Arguments

Value

A character vector of node names.

Helper to expand the right-hand side of an edge specification

Description

This function expands the right-hand side of an edgespecification into a character vector of target node names. It handlesvarious forms of input, including symbols, calls with+, calls withc(),and character literals.

Usage

.expand_targets(expr)

Arguments

Value

A character vector of target node names.

Flatten a chained edge expression

Description

Given a chained edge expression,flatten it into its terms and operators.

Usage

.flatten_edge_chain(call_expr)

Arguments

Value

A list with two elements,terms andops.

Build edgesdata.table from verb call.

Description

Internal helper to build edgesdata.table from verb call.

Usage

.get_edges(from, edge, to, calls)

Arguments

Value

Adata.table with columnsfrom,edge, andto.

Get nodesdata.table from verb call.

Description

Internal helper to build nodesdata.table from verb call.

Usage

.get_nodes(name, calls)

Arguments

Value

Adata.table with columnname for node names.

Output object of getter queries

Description

Helper to format the output of getter queries.

Usage

.getter_output(cg, idx0, nodes)

Arguments

Value

A list of character vectors, each a set of node names.If only one node is requested, returns a character vector.

Get edge operators

Description

This function gets the default caugi edge glyphs

Usage

.glyph_map_get()

Value

The current edge glyphs of the caugi environment

Get glyph for an operator

Description

Get the glyph string for a given edge operator symbol.

Usage

.glyph_of(op_sym)

Arguments

Value

A string representing the edge glyph (e.g.,"-->").

Is it an edge call / expression?

Description

This function checks if the expression is an edge call

Usage

.is_edge_call(expr)

Arguments

Value

TRUE if the expression is an edge call, FALSE otherwise

Is it a node expr?

Description

Check if the expression is a valid node expression:symbol, string, number, c(...), +, (...)

Usage

.is_node_expr(expr)

Arguments

Value

TRUE if the expression is a valid node expression, FALSE otherwise

Mark acaugi asnot built.

Description

When acaugi is modified, it should be marked as notbuilt. This function sets thebuilt attribute toFALSE. Thereby, the Rustbackend and the R frontend does not match, and at one point, thecaugi will need to be rebuild for it to be queried.

Usage

.mark_not_built(cg)

Arguments

Value

The samecaugi object, but with thebuilt attribute set toFALSE.

Node constructor

Description

A simple wrapper creating adata.table object with a single columnname.

Usage

.node_constructor(names = character(), sort = FALSE)

Arguments

Details

The reason this exists is so if changes should be made in the future, it iseasy to simply change this constructor, rather than changing the calls todata.table all over the place.

Value

Adata.table object with a single columnname.

Parse one caugi(...) argument

Description

Parse one caugi(...) argument into edge units

Usage

.parse_edge_arg(expr)

Arguments

Value

A list of edge units, each with lhs, rhs, and glyph.

Register a new edge operator

Description

Register a new edge operator for use incaugi().

Usage

.register_edge(glyph)

Arguments

Value

The operator name (e.g.,"%-->%"), invisibly.

Resolve node name or index to 0-based index.

Description

Internal helper function to resolve either a node name or anode index to a 0-based index..resolve_idx0_get usesget on thefastmap and expects a single value,while.resolve_idx0_mget usesmget and can return multiple values.

Usage

.resolve_idx0_get(nm_idx_map, node_name = NULL, node_index = NULL).resolve_idx0_mget(nm_idx_map, node_name = NULL, node_index = NULL)

Arguments

See Also

fastmap::fastmap

Create an edge unit from lhs, op, rhs

Description

Create an edge unit from lhs, op, rhs expressions.

Usage

.segment_units(lhs_term, op_chr, rhs_term)

Arguments

Value

A list with elementslhs,rhs, andglyph.

Set names to an object

Description

Only made to avoid usingstats::setNames.

Usage

.set_names(object = nm, nm)

Arguments

Value

The input object with the assigned names.

Expand target expressions with=

Description

Split any expression into top-level '+' terms (fully flattened).

Usage

.split_plus(expr)

Arguments

Value

A character vector of node names.

Update nodes and edges of acaugi

Description

Internal helper to add or remove nodes/edges and mark graph asnot built.

Usage

.update_caugi(  cg,  nodes = NULL,  edges = NULL,  action = c("add", "remove"),  inplace = FALSE)

Arguments

Value

The updatedcaugi object.

Convert a graph pointer to acaugi S7 object

Description

Convert a graph pointer from Rust to acaugi to aS7 object.

Usage

.view_to_caugi(ptr, node_names = NULL)

Arguments

Value

Acaugi object representing the graph.

Compute an adjustment set

Description

Computes an adjustment set forX -> Y in a DAG.

Usage

adjustment_set(  cg,  X = NULL,  Y = NULL,  X_index = NULL,  Y_index = NULL,  type = c("optimal", "parents", "backdoor"))

Arguments

Details

Types supported:

• "parents":\bigcup \mathrm{Pa}(X) minusX \cup Y

• "backdoor": Pearl backdoor formula

• "optimal": O-set (only for singlex and singley)

Value

A character vector of node names representing the adjustment set.

See Also

Other adjustment:all_backdoor_sets(),d_separated(),is_valid_backdoor()

Examples

cg <- caugi(  C %-->% X,  X %-->% F,  X %-->% D,  A %-->% X,  A %-->% K,  K %-->% Y,  D %-->% Y,  D %-->% G,  Y %-->% H,  class = "DAG")adjustment_set(cg, "X", "Y", type = "parents") # C, Aadjustment_set(cg, "X", "Y", type = "backdoor") # C, Aadjustment_set(cg, "X", "Y", type = "optimal") # K

Adjustment Identification Distance

Description

Compute the Adjustment Identification Distance (AID) between twographs using thegadjid Rust package.

Usage

aid(truth, guess, type = c("oset", "ancestor", "parent"), normalized = TRUE)

Arguments

Value

A numeric representing the AID between the two graphs, ifnormalized = TRUE, or an integer count ifnormalized = FALSE.

See Also

Other metrics:hd(),shd()

Examples

set.seed(1)truth <- generate_graph(n = 100, m = 200, class = "DAG")guess <- generate_graph(n = 100, m = 200, class = "DAG")aid(truth, guess) # 0.0187

Get all backdoor sets up to a certain size.

Description

This function returns the backdoor sets up to sizemax_size,which per default is set to 10.

Usage

all_backdoor_sets(  cg,  X = NULL,  Y = NULL,  X_index = NULL,  Y_index = NULL,  minimal = TRUE,  max_size = 3L)

Arguments

Value

A list of character vectors, each an adjustment set(possibly empty).

See Also

Other adjustment:adjustment_set(),d_separated(),is_valid_backdoor()

Examples

cg <- caugi(  C %-->% X,  X %-->% F,  X %-->% D,  A %-->% X,  A %-->% K,  K %-->% Y,  D %-->% Y,  D %-->% G,  Y %-->% H,  class = "DAG")all_backdoor_sets(cg, X = "X", Y = "Y", max_size = 3L, minimal = FALSE)#> [[1]]#> [1] "A"#>#> [[2]]#> [1] "K"#>#> [[3]]#> [1] "C" "A"#>#> [[4]]#> [1] "C" "K"#>#> [[5]]#> [1] "A" "K"#>#> [[6]]#> [1] "C" "A" "K"all_backdoor_sets(cg, X = "X", Y = "Y", max_size = 3L, minimal = TRUE)#> [[1]]#> [1] "A"#>#> [[2]]#> [1] "K"

Get ancestors of nodes in acaugi

Description

Get ancestors of nodes in acaugi

Usage

ancestors(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")ancestors(cg, "A") # NULLancestors(cg, index = 2) # "A"ancestors(cg, "B") # "A"ancestors(cg, c("B", "C"))#> $B#> [1] "A"#>#> $C#> [1] "A" "B"

Convert a caugi to an adjacency matrix

Description

Does not take other edge types than the one found in a PDAG.

Usage

as_adjacency(x)

Arguments

Value

An integer 0/1 adjacency matrix with row/col names.

See Also

Other conversions:as_bnlearn(),as_caugi(),as_dagitty(),as_igraph()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")adj <- as_adjacency(cg)

Convert a caugi to a bnlearn network

Description

Convert a caugi to a bnlearn network

Usage

as_bnlearn(x)

Arguments

Value

Abnlearn DAG.

See Also

Other conversions:as_adjacency(),as_caugi(),as_dagitty(),as_igraph()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")g_bn <- as_bnlearn(cg)

Convert to acaugi

Description

Convert an object to acaugi. The object can be agraphNEL,matrix,tidygraph,daggity,bn, origraph.

Usage

as_caugi(  x,  class = c("DAG", "PDAG", "PAG", "UNKNOWN"),  simple = TRUE,  build = TRUE,  collapse = FALSE,  collapse_to = "---",  ...)

Arguments

Details

For matrices,as_caugi assumes that the rows are thefrom nodesand the columns are theto nodes. Thus, for a graph, G: A –> B, we wouldhave thatG["A", "B"] == 1 andG["B", "A"] == 0.For PAGs, the integer codes are as follows (as used inpcalg):

• 0: no edge

• 1: circle (e.g.,⁠A o-o B⁠ or⁠A o-- B⁠)

• 2: arrowhead (e.g.,⁠A --> B⁠ or⁠A o-> B⁠)

• 3: tail (e.g.,⁠A o-- B⁠ orA --- B)

Value

Acaugi object.

See Also

Other conversions:as_adjacency(),as_bnlearn(),as_dagitty(),as_igraph()

Examples

# igraphig <- igraph::graph_from_literal(A - +B, B - +C)cg_ig <- as_caugi(ig, class = "DAG")# graphNELgn <- graph::graphNEL(nodes = c("A", "B", "C"), edgemode = "directed")gn <- graph::addEdge("A", "B", gn)gn <- graph::addEdge("B", "C", gn)cg_gn <- as_caugi(gn, class = "DAG")# adjacency matrixm <- matrix(0L, 3, 3, dimnames = list(LETTERS[1:3], LETTERS[1:3]))m["A", "B"] <- 1Lm["B", "C"] <- 1Lcg_adj <- as_caugi(m, class = "DAG")# bnlearnbn <- bnlearn::model2network("[A][B|A][C|B]")cg_bn <- as_caugi(bn, class = "DAG")# dagittydg <- dagitty::dagitty("dag { A -> B B -> C }")cg_dg <- as_caugi(dg, class = "DAG")cg <- caugi(A %-->% B %-->% C, class = "DAG")# check that all nodes are equal in all graph objectsfor (cg_converted in list(cg_ig, cg_gn, cg_adj, cg_bn, cg_dg)) {  stopifnot(identical(nodes(cg), nodes(cg_converted)))  stopifnot(identical(edges(cg), edges(cg_converted)))}# collapse mutual edgesig2 <- igraph::graph_from_literal(A - +B, B - +A, C - +D)cg2 <- as_caugi(ig2, class = "PDAG", collapse = TRUE, collapse_to = "---")# coded integer matrix for PAGs (pcalg style)nm <- c("A", "B", "C", "D")M <- matrix(0L, 4, 4, dimnames = list(nm, nm))# A --> BM["A", "B"] <- 2L # mark at B endM["B", "A"] <- 3L # mark at A end# A --- CM["A", "C"] <- 3LM["C", "A"] <- 3L# B o-> CM["B", "C"] <- 2LM["C", "B"] <- 1L# C o-o DM["C", "D"] <- 1LM["D", "C"] <- 1Lcg <- as_caugi(M, class = "PAG")

Convert a caugi to a dagitty graph

Description

Convert a caugi to a dagitty graph

Usage

as_dagitty(x)

Arguments

Value

Adagitty object.

See Also

Other conversions:as_adjacency(),as_bnlearn(),as_caugi(),as_igraph()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")g_dg <- as_dagitty(cg)

Convert a caugi to an igraph object

Description

Convert a caugi to an igraph object

Usage

as_igraph(x, ...)

Arguments

Value

Anigraph object representing the same graph structure.

See Also

Other conversions:as_adjacency(),as_bnlearn(),as_caugi(),as_dagitty()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")ig <- as_igraph(cg)

Build the graph now

Description

If acaugi has been modified (nodes or edges added orremoved), it is marked asnot built, i.ecg@built = FALSE.This function builds the graph using the Rust backend and updates theinternal pointer to the graph. If the graph is already built, it is returned.

Usage

build(cg, ...)

Arguments

Value

The builtcaugi object.

See Also

Other verbs:caugi_verbs

Examples

# initialize empty graph and build slowlycg <- caugi(class = "PDAG")cg <- cg |>  add_nodes(c("A", "B", "C", "D", "E")) |> # A, B, C, D, E  add_edges(A %-->% B %-->% C) |> # A --> B --> C, D, E  set_edges(B %---% C) # A --> B --- C, D, Ecg <- remove_edges(cg, B %---% C) |> # A --> B, C, D, E  remove_nodes(c("C", "D", "E")) # A --> B# verbs do not build the Rust backendcg@built # FALSEbuild(cg)cg@built # TRUE

Create acaugi from edge expressions.

Description

Create acaugi from a series of edge expressions usinginfix operators. Nodes can be specified as symbols, strings, or numbers.

The following edge operators are supported by default:

• ⁠%-->%⁠ for directed edges (A –> B)

• ⁠%---%⁠ for undirected edges (A — B)

• ⁠%<->%⁠ for bidirected edges (A <-> B)

• ⁠%o->%⁠ for partially directed edges (A o-> B)

• ⁠%--o%⁠ for partially undirected edges (A –o B)

• ⁠%o-o%⁠ for partial edges (A o-o B)

You can register additional edge types usingregister_caugi_edge().

Usage

caugi(  ...,  from = NULL,  edge = NULL,  to = NULL,  nodes = NULL,  edges_df = NULL,  simple = TRUE,  build = TRUE,  class = c("UNKNOWN", "DAG", "PDAG", "UG"),  state = NULL)

Arguments

Value

Acaugi S7 object containing the nodes, edges, and apointer to the underlying Rust graph structure.

Examples

# create a simple DAG (using NSE)cg <- caugi(  A %-->% B + C,  B %-->% D,  class = "DAG")# create a PDAG with undirected edges (using NSE)cg2 <- caugi(  A %-->% B + C,  B %---% D,  E, # no neighbors for this node  class = "PDAG")# create a DAG (using SE)cg3 <- caugi(  from = c("A", "A", "B"),  edge = c("-->", "-->", "-->"),  to = c("B", "C", "D"),  nodes = c("A", "B", "C", "D", "E"),  class = "DAG")# create a non-simple graphcg4 <- caugi(  A %-->% B,  B %-->% A,  class = "UNKNOWN",  simple = FALSE)cg4@simple # FALSEcg4@built # TRUEcg4@graph_class # "UNKNOWN"# create graph, but don't built Rust object yet, which is needed for queriescg5 <- caugi(  A %-->% B + C,  B %-->% D,  class = "DAG",  build = FALSE)cg@built # FALSE

Manipulate nodes and edges of acaugi

Description

Add, remove, or and set nodes or edges to / from acaugiobject. Edges can be specified using expressions with the infix operators.Alternatively, the edges to be added are specified using thefrom,edge, andto arguments.

Usage

add_edges(cg, ..., from = NULL, edge = NULL, to = NULL, inplace = FALSE)remove_edges(cg, ..., from = NULL, edge = NULL, to = NULL, inplace = FALSE)set_edges(cg, ..., from = NULL, edge = NULL, to = NULL, inplace = FALSE)add_nodes(cg, ..., name = NULL, inplace = FALSE)remove_nodes(cg, ..., name = NULL, inplace = FALSE)

Arguments

Details

Caugi graph verbs

Value

The updatedcaugi.

Functions

• add_edges(): Add edges.

• remove_edges(): Remove edges.

• set_edges(): Set edge type for given pair(s).

• add_nodes(): Add nodes.

• remove_nodes(): Remove nodes.

See Also

Other verbs:build()

Examples

# initialize empty graph and build slowlycg <- caugi(class = "PDAG")cg <- cg |>  add_nodes(c("A", "B", "C", "D", "E")) |> # A, B, C, D, E  add_edges(A %-->% B %-->% C) |> # A --> B --> C, D, E  set_edges(B %---% C) # A --> B --- C, D, Ecg <- remove_edges(cg, B %---% C) |> # A --> B, C, D, E  remove_nodes(c("C", "D", "E")) # A --> B# verbs do not build the Rust backendcg@built # FALSEbuild(cg)cg@built # TRUE

Get children of nodes in acaugi

Description

Get children of nodes in acaugi

Usage

children(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")children(cg, "A") # "B"children(cg, index = 2) # "C"children(cg, "B") # "C"children(cg, c("B", "C"))#> $B#> [1] "C"#>#> $C#> NULL

Are X and Y d-separated given Z?

Description

Checks whether every node inX is d-separated from every nodeinY givenZ in a DAG.

Usage

d_separated(  cg,  X = NULL,  Y = NULL,  Z = NULL,  X_index = NULL,  Y_index = NULL,  Z_index = NULL)

Arguments

Value

TRUE if d-separated,FALSE otherwise.

See Also

Other adjustment:adjustment_set(),all_backdoor_sets(),is_valid_backdoor()

Examples

cg <- caugi(  C %-->% X,  X %-->% F,  X %-->% D,  A %-->% X,  A %-->% K,  K %-->% Y,  D %-->% Y,  D %-->% G,  Y %-->% H,  class = "DAG")d_separated(cg, "X", "Y", Z = c("A", "D")) # TRUEd_separated(cg, "X", "Y", Z = NULL) # FALSE

Get descendants of nodes in acaugi

Description

Get descendants of nodes in acaugi

Usage

descendants(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),children(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")descendants(cg, "A") # "B" "C"descendants(cg, index = 2) # "C"descendants(cg, "B") # "C"descendants(cg, c("B", "C"))#> $B#> [1] "C"#>#> $C#> NULL

Infix operators for edge specifications

Description

These operators are used to specify edges incaugi().Should be used internally incaugi() calls.

Usage

lhs %-->% rhslhs %---% rhslhs %<->% rhslhs %o-o% rhslhs %--o% rhslhs %o->% rhs

Arguments

Value

Adata.table with columnsfrom,to, andedge.

Get the edge types of acaugi.

Description

Get the edge types of acaugi.

Usage

edge_types(cg)

Arguments

Value

A character vector of edge types.

See Also

Other queries:ancestors(),children(),descendants(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %--o% C,  C %<->% D,  D %---% E,  A %o-o% E,  class = "UNKNOWN")edge_types(cg) # returns c("-->", "o-o", "--o", "<->", "---")

Get edges of acaugi.

Description

Get edges of acaugi.

Usage

edges(cg)E(cg)

Arguments

Value

Adata.table with columnsfrom,edge, andto.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  D,  class = "DAG")edges(cg) # returns the data.table with columns from, edge, to

Get all exogenous nodes in acaugi

Description

Get all exogenous nodes (nodes with no parents) in acaugi.

Usage

exogenous(cg, undirected_as_parents = FALSE)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")exogenous(cg) # "A"

Generate acaugi using Erdős-Rényi.

Description

Sample a random DAG or CPDAG usingErdős-Rényi for random graph generation.

Usage

generate_graph(n, m = NULL, p = NULL, class = c("DAG", "CPDAG"))

Arguments

Value

The sampledcaugi object.

Examples

# generate a random DAG with 5 nodes and 4 edgesdag <- generate_graph(n = 5, m = 4, class = "DAG")# generate a random CPDAG with 5 nodes and edge probability 0.3cpdag <- generate_graph(n = 5, p = 0.3, class = "CPDAG")

Hamming Distance

Description

Compute the Hamming Distance between two graphs.

Usage

hd(cg1, cg2, normalized = FALSE)

Arguments

Value

An integer representing the Hamming Distance between the two graphs,ifnormalized = FALSE, or a numeric between 0 and 1 ifnormalized = TRUE.

See Also

Other metrics:aid(),shd()

Examples

cg1 <- caugi(A %-->% B %-->% C, D %-->% C, class = "DAG")cg2 <- caugi(A %-->% B %-->% C, D %---% C, class = "PDAG")hd(cg1, cg2) # 0

Is thecaugi acyclic?

Description

Checks if the givencaugi graph is acyclic.

Usage

is_acyclic(cg, force_check = FALSE)

Arguments

Details

Logically, it should not be possible to have a graph class of "DAG" or "PDAG"that has cycles, but in case the user modified the graph after creation insome unforeseen way that could have introduced cycles, this function allowsto force a check of acyclicity, if needed.

Value

A logical value indicating whether the graph is acyclic.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_acyclic <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")is_acyclic(cg_acyclic) # TRUEcg_cyclic <- caugi(  A %-->% B,  B %-->% C,  C %-->% A,  class = "UNKNOWN")is_acyclic(cg_cyclic) # FALSE

Is it acaugi graph?

Description

Checks if the given object is acaugi. Mostly usedinternally to validate inputs.

Usage

is_caugi(x, throw_error = FALSE)

Arguments

Value

A logical value indicating whether the object is acaugi.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")is_caugi(cg) # TRUE

Is thecaugi graph a CPDAG?

Description

Checks if the givencaugi graph is aComplete Partially Directed Acyclic Graph (CPDAG).

Usage

is_cpdag(cg)

Arguments

Value

A logical value indicating whether the graph is a CPDAG.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_cpdag <- caugi(  A %---% B,  A %-->% C,  B %-->% C,  class = "PDAG")is_cpdag(cg_cpdag) # TRUEcg_not_cpdag <- caugi(  A %---% B,  A %---% C,  B %-->% C,  class = "PDAG")is_cpdag(cg_not_cpdag) # FALSE

Is thecaugi graph a DAG?

Description

Checks if the givencaugi graph is aDirected Acyclic Graph (DAG).

Usage

is_dag(cg, force_check = FALSE)

Arguments

Value

A logical value indicating whether the graph is a DAG.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_dag_class <- caugi(  A %-->% B,  class = "DAG")is_dag(cg_dag_class) # TRUEcg_dag_but_pdag_class <- caugi(  A %-->% B,  class = "PDAG")is_dag(cg_dag_but_pdag_class) # TRUEcg_cyclic <- caugi(  A %-->% B,  B %-->% C,  C %-->% A,  class = "UNKNOWN",  simple = FALSE)is_dag(cg_cyclic) # FALSEcg_undirected <- caugi(  A %---% B,  class = "UNKNOWN")is_dag(cg_undirected) # FALSE

Is the edge symmetric?

Description

Check if the given edge glyph is symmetric in the edge registry.

Usage

is_edge_symmetric(glyph)

Arguments

Value

Logical,TRUE if the edge is symmetric, otherwise throws error.

Is thecaugi graph empty?

Description

Checks if the givencaugi graph is empty (has no nodes).

Usage

is_empty_caugi(cg)

Arguments

Value

A logical value indicating whether the graph is empty.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_empty <- caugi(class = "DAG")is_empty_caugi(cg_empty) # TRUEcg_non_empty <- caugi(  A %-->% B,  class = "DAG")is_empty_caugi(cg_non_empty) # FALSEcg_no_edges_but_has_nodes <- caugi(  A, B,  class = "DAG")is_empty_caugi(cg_no_edges_but_has_nodes) # FALSE

Is thecaugi graph a PDAG?

Description

Checks if the givencaugi graph is aPartially Directed Acyclic Graph (PDAG).

Usage

is_pdag(cg, force_check = FALSE)

Arguments

Value

A logical value indicating whether the graph is a PDAG.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_dag_class <- caugi(  A %-->% B,  class = "DAG")is_pdag(cg_dag_class) # TRUEcg_dag_but_pdag_class <- caugi(  A %-->% B,  class = "PDAG")is_pdag(cg_dag_but_pdag_class) # TRUEcg_cyclic <- caugi(  A %-->% B,  B %-->% C,  C %-->% A,  D %---% A,  class = "UNKNOWN",  simple = FALSE)is_pdag(cg_cyclic) # FALSEcg_undirected <- caugi(  A %---% B,  class = "UNKNOWN")is_pdag(cg_undirected) # TRUEcg_pag <- caugi(  A %o->% B,  class = "UNKNOWN")is_pdag(cg_pag) # FALSE

Is thecaugi graph an UG?

Description

Checks if the givencaugi graph is an undirected graph (UG).

Usage

is_ug(cg, force_check = FALSE)

Arguments

Value

A logical value indicating whether the graph is an UG.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg_ug_class <- caugi(  A %---% B,  class = "UG")is_ug(cg_ug_class) # TRUEcg_not_ug <- caugi(  A %-->% B,  class = "DAG")is_ug(cg_not_ug) # FALSE

Is a backdoor set valid?

Description

Checks whetherZ is a valid backdoor adjustment set for⁠X --> Y⁠.

Usage

is_valid_backdoor(  cg,  X = NULL,  Y = NULL,  Z = NULL,  X_index = NULL,  Y_index = NULL,  Z_index = NULL)

Arguments

Value

Logical value indicating if backdoor is valid or not.

See Also

Other adjustment:adjustment_set(),all_backdoor_sets(),d_separated()

Examples

cg <- caugi(  C %-->% X,  X %-->% F,  X %-->% D,  A %-->% X,  A %-->% K,  K %-->% Y,  D %-->% Y,  D %-->% G,  Y %-->% H,  class = "DAG")is_valid_backdoor(cg, X = "X", Y = "Y", Z = NULL) # FALSEis_valid_backdoor(cg, X = "X", Y = "Y", Z = "K") # TRUEis_valid_backdoor(cg, X = "X", Y = "Y", Z = c("A", "C")) # TRUE

Length of acaugi

Description

Returns the number of nodes in the graph.

Arguments

Value

An integer representing the number of nodes.

See Also

Other caugi methods:print()

Examples

cg <- caugi(  A %-->% B,  class = "DAG")length(cg) # 2cg2 <- caugi(  A %-->% B + C,  nodes = LETTERS[1:5],  class = "DAG")length(cg2) # 5

Get Markov blanket of nodes in acaugi

Description

Get Markov blanket of nodes in acaugi

Usage

markov_blanket(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),neighbors(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")markov_blanket(cg, "A") # "B"markov_blanket(cg, index = 2) # "A" "C"markov_blanket(cg, "B") # "A" "C"markov_blanket(cg, c("B", "C"))#> $B#> [1] "A" "C"#>#> $C#> [1] "B"

Moralize a DAG

Description

Moralizing a DAG involves connecting all parents of each node and thenconverting all directed edges into undirected edges.

Usage

moralize(cg)

Arguments

Details

This changes the graph from a Directed Acyclic Graph (DAG) to anUndirected Graph (UG), also known as a Markov Graph.

Value

Acaugi object representing the moralized graph (UG).

See Also

Other operations:mutate_caugi(),skeleton()

Examples

cg <- caugi(A %-->% C, B %-->% C, class = "DAG")moralize(cg) # A -- B, A -- C, B -- C

Mutatecaugi class

Description

Mutate thecaugi class from one graph class to another, if possible.For example, convert aDAG to aPDAG, or a fully directedcaugi ofclassUNKNOWN to aDAG. Throws an error if not possible.

Usage

mutate_caugi(cg, class)

Arguments

Details

This function returns a copy of the object, and the original remainsunchanged.

Value

Acaugi object of the specified class.

See Also

Other operations:moralize(),skeleton()

Examples

cg <- caugi(A %-->% B, class = "UNKNOWN")cg_dag <- mutate_caugi(cg, "DAG")

Get neighbors of nodes in acaugi

Description

Get neighbors of nodes in acaugi

Usage

neighbors(cg, nodes = NULL, index = NULL)neighbours(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),nodes(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")neighbors(cg, "A") # "B"neighbors(cg, index = 2) # "A" "C"neighbors(cg, "B") # "A" "C"neighbors(cg, c("B", "C"))#> $B#> [1] "A" "C"#>#> $C#> [1] "B"

Get nodes or edges of acaugi

Description

Get nodes or edges of acaugi

Usage

nodes(cg)vertices(cg)V(cg)

Arguments

Value

Adata.table with aname column.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),parents(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  D,  class = "DAG")nodes(cg) # returns the data.table with nodes A, B, C, D

Get parents of nodes in acaugi

Description

Get parents of node in a graph. Note that not both nodes and index can begiven.

Usage

parents(cg, nodes = NULL, index = NULL)

Arguments

Value

Either a character vector of node names (if a single node isrequested) or a list of character vectors (if multiple nodes are requested).

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),same_nodes(),subgraph()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")parents(cg, "A") # NULLparents(cg, index = 2) # "A"parents(cg, "B") # "A"parents(cg, c("B", "C"))#> $B#> [1] "A"#>#> $C#> [1] "B"

Print acaugi

Description

Print acaugi

Arguments

Value

The inputcaugi object, invisibly.

See Also

Other caugi methods:length()

Examples

cg <- caugi(A %-->% B, class = "DAG")print(cg)

Register a new edge type in the global registry.

Description

Register a new edge type in the global registry.

Usage

register_caugi_edge(glyph, tail_mark, head_mark, class, symmetric = FALSE)

Arguments

Value

TRUE, invisibly.

See Also

Other registry:registry

Examples

# first, for reproducability, we reset the registry to defaultreset_caugi_registry()# create a new registryreg <- caugi_registry()# register an edgeregister_caugi_edge(  glyph = "<--",  tail_mark = "arrow",  head_mark = "tail",  class = "directed",  symmetric = FALSE)# now, this edge is available for caugi graphs:cg <- caugi(A %-->% B, B %<--% C, class = "DAG")# reset the registry to defaultreset_caugi_registry()

caugi edge registry

Description

Thecaugi edge registry stores information about the different edge typesthat can be used incaugi graphs. It maps edge glyphs (e.g.,"-->","<->","o->", etc.) to their specifications, including tail and headmarks, class, and symmetry. The registry allows for dynamic registration ofnew edge types, enabling users to extend the set of supported edges incaugi. It is implemented as a singleton, ensuring that there is a singleglobal instance of the registry throughout the R session.

Usage

caugi_registry()reset_caugi_registry()seal_caugi_registry()

Details

The intented use of thecaugi registry is mostly for advanced users anddevelopers. The registry enables users who need to define their own customedge types incaugi directly. . It currently mostly supports therepresentation of new edges, but for users that might want to representreverse edges, this preserves correctness of reason over these edges.

Value

Anedge_registry external pointer.

Functions

• caugi_registry(): Access the global edge registry, creating it if needed.

• reset_caugi_registry(): Reset the global edge registry to its default state.

• seal_caugi_registry(): Seal the global edge registry to prevent furthermodifications.

See Also

Other registry:register_caugi_edge()

Examples

# first, for reproducability, we reset the registry to defaultreset_caugi_registry()# create a new registryreg <- caugi_registry()# register an edgeregister_caugi_edge(  glyph = "<--",  tail_mark = "arrow",  head_mark = "tail",  class = "directed",  symmetric = FALSE)# now, this edge is available for caugi graphs:cg <- caugi(A %-->% B, B %<--% C, class = "DAG")# reset the registry to defaultreset_caugi_registry()

Same nodes?

Description

Check if twocaugi objects have the same nodes.

Usage

same_nodes(cg1, cg2, throw_error = FALSE)

Arguments

Value

A logical indicating if the two graphs have the same nodes.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),subgraph()

Examples

cg1 <- caugi(  A %-->% B,  class = "DAG")cg2 <- caugi(  A %-->% B + C,  class = "DAG")same_nodes(cg1, cg2) # FALSE

Structural Hamming Distance

Description

Compute the Structural Hamming Distance (SHD) between twographs.

Usage

shd(cg1, cg2, normalized = FALSE)

Arguments

Value

An integer representing the Hamming Distance between the two graphs,ifnormalized = FALSE, or a numeric between 0 and 1 ifnormalized = TRUE.

See Also

Other metrics:aid(),hd()

Examples

cg1 <- caugi(A %-->% B %-->% C, D %-->% C, class = "DAG")cg2 <- caugi(A %-->% B %-->% C, D %---% C, class = "PDAG")shd(cg1, cg2) # 1

Get the skeleton of a graph

Description

The skeleton of a graph is obtained by replacing all directed edges withundirected edges.

Usage

skeleton(cg)

Arguments

Details

This changes the graph from any class to an Undirected Graph (UG), also knownas a Markov Graph.

Value

Acaugi object representing the skeleton of the graph (UG).

See Also

Other operations:moralize(),mutate_caugi()

Examples

cg <- caugi(A %-->% B, class = "DAG")skeleton(cg) # A --- B

Get the induced subgraph

Description

Get the induced subgraph

Usage

subgraph(cg, nodes = NULL, index = NULL)

Arguments

Value

A newcaugi that is a subgraph of the selected nodes.

See Also

Other queries:ancestors(),children(),descendants(),edge_types(),edges(),exogenous(),is_acyclic(),is_caugi(),is_cpdag(),is_dag(),is_empty_caugi(),is_pdag(),is_ug(),markov_blanket(),neighbors(),nodes(),parents(),same_nodes()

Examples

cg <- caugi(  A %-->% B,  B %-->% C,  class = "DAG")sub_cg <- subgraph(cg, c("B", "C"))cg2 <- caugi(B %-->% C, class = "DAG")all(nodes(sub_cg) == nodes(cg2)) # TRUEall(edges(sub_cg) == edges(cg2)) # TRUE