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-->), bidirected (<->), and
undirected (---) edges while satisfying ancestral graph
constraints. New functions: is_ag(),
is_mag().mode argument to
neighbors()/neighbours() to filter neighbors
by edge direction or type ("all", "in",
"out", "undirected",
"bidirected", "partial"). This is a structural
query, and not a semantic query!neighbors() now supports class = "UNKNOWN"
graphs, including mode-based filtering.simulate_data() that enables simulation from DAGs
using SEMs. Standard linear Gaussian SEMs are defaults, but more
importantly custom SEMs are available."AUTO" parameter for class in
caugi objects. This automatically picks the graph class in
order DAG, UG, PDAG,
ADMG, AG.is_ag() and
is_mag() and m-separation for AGs.exogenize() function that exogenizes variables for
any graph type. Current implementation is written in R, but it is so
simple that it might be preferable over a Rust implementation. This
might be changed later.latent_project() function that does latent
projection from DAGs to ADMGs.write_caugi(), read_caugi(),
caugi_serialize(), and caugi_deserialize().
The format is a versioned JSON schema that captures graph structure,
class, and optional metadata (comments and tags).plot() method for visualizing graphs using various
layout algorithms. The plot is rendered using grid graphics and returns
a caugi_plot object that can be customized with
node_style, edge_style, and
label_style arguments. The plot() method
accepts layouts as strings, functions, or pre-computed data.frames.caugi_layout() function to compute node coordinates
for graph visualization.caugi_layout_sugiyama(),
caugi_layout_fruchterman_reingold(),
caugi_layout_kamada_kawai(),
caugi_layout_bipartite(), and
caugi_layout_tiered(). Each function provides an API for
its specific algorithm.to_dot() and write_dot() functions for
exporting caugi graphs to DOT (graphviz) format. The resulting object is
a new S7 class, caugi_export, which has a
knit_print() method for rendering DOT graphs in R Markdown
and Quarto documents.to_graphml, write_graphml,
read_graphml, to_mermaid,
write_mermaid, and read_mermaid+ and | for horizontal arrangement,
/ for vertical stacking. Compositions can be nested
arbitrarily (e.g., (p1 + p2) / p3).caugi_options() function for setting global
defaults for plot appearance, including composition spacing and default
styles for nodes, edges, labels, and titles.caugi_default_options() function to query or reset
to package default options.is_caugi() validation calls
internally.caugi_layout_tiered() now returns a tier
column and orientation attribute in the layout data.frame,
allowing plot() to automatically use tier information
without requiring the tiers argument to be passed
again.index_name_map parameter from internal
.cg_state() function.is_cpdag function that returns
TRUE on non-complete PDAGs.shd returning positive values for equivalent
graphs given in shuffled order.is_admg(),
spouses(), districts(), and
m_separated() (generalization of d-separation for graphs
with bidirected edges).is_valid_adjustment_admg() and
all_adjustment_sets_admg() implementing the Generalized
Adjustment Criterion.mutate_caugi() function that allows conversion from
one graph type to another.caugi objects.edges_df argument to caugi()
for easier construction from existing data frames containing the columns
from, edge, and to.as_adjacency() and as_igraph() to
support bidirected edges.as_caugi() documentation to include “ADMG” as a
valid class type for conversion.caugi.org/.CONTRIBUTING.md.README rewrite.lockBinding and
unlockBinding in the package to silence R CMD check
notes.mutate_caugi function that allows conversion from
one graph type to another.edges_df argument to caugi
for easier construction from existing data frames containing the columns
from, edge, and to.caugi in a package vignette
to use new conversion functionality.CONTRIBUTING.md to github.as_caugi.dplyr and
tibble.data.table.data.tables.caugi.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.