High-Performance Open-Source Archive
caugi_layout_circle() and a "circle"
method for caugi_layout() that places nodes evenly along
the perimeter of a circle (#108).list_caugi_edges() function to list all available
edge types."MPDAG" graph class support across
constructor, class mutation, and class resolution.
class = "AUTO" now resolves Meek-closed PDAGs to
"MPDAG".exogenize() is now implemented in Rust for DAGs, which
reduces overhead on larger graphs.normalize_latent_structure() is now implemented in Rust
for DAGs for faster latent normalization workflows.minimal_d_separator() is renamed to
minimal_separator() and now supports ADMG and AG inputs
(previously DAG-only), returning a minimal m-separator. Implemented via
the unified linear-time algorithm of van der Zander & Liśkiewicz
(UAI 2020). The old name minimal_d_separator() remains as a
deprecated alias.m_separated() on ADMGs: moralization now marries
every pair in pa(v) ∪ sp(v), not just pa(v).
The old code missed moral edges from bidirected co-parents and gave
false positives (e.g. claimed Z ⊥ Y | X for
Z -> X -> Y, X <-> Y).is_valid_adjustment_admg() and
all_adjustment_sets_admg() to verify the GAC’s m-separation
condition in the proper backdoor graph rather than via a per-neighbour
decomposition. The old check trivially accepted neighbours of
X that were themselves in Z, so it falsely
classified {C} as a valid adjustment set in the M-bias ADMG
C -> X, C <-> X, C -> Y, C <-> Y, X -> Y
(#277).normalize_latent_structure(), which normalizes the
latent structure of a DAG while preserving the marginal model over
observed variables.minimal_d_separator(), which computes a minimal
d-separator between sets of nodes in a DAG, with support for mandatory
inclusions and restrictions.posteriors() query function, which is the dual of
anteriors(). It returns all nodes reachable by following
paths where every edge is either undirected or directed away from the
source node. For DAGs, posteriors() equals
descendants(). For PDAGs and AGs, it includes both
descendants and nodes reachable via undirected edges.ancestors(),
anteriors(), descendants(), and
posteriors(). This can be set globally with
caugi_options() or locally with the
open = TRUE/FALSE argument. The default remains
open = TRUE.is_mpdag() query to check whether a PDAG is closed
under Meek’s orientation rules (R1-R4), and meek_closure()
to orient all implied edges until Meek closure.caugi_options() now supports nested key drilling:
multiple unnamed arguments traverse nested options (e.g.,
caugi_options("plot", "tier_style", "fill")).simple, graph_class,
nodes, edges) are sourced from the
session.
@.state,
@name_index_map, @built, and @ptr
now warn on access and return NULL.build
and state in caugi() now warn and are
ignored.inplace parameter in verb functions
(add_edges(), remove_edges(),
set_edges(), add_nodes(),
remove_nodes()). All graph modifications now use
copy-on-write semantics for consistency with R conventions. The
parameter is deprecated and ignored with a warning.all.equal and compare_proxy methods
for caugi objects to support graph-content comparison in tests.asp parameter to plot() for
controlling the aspect ratio. When asp = 1, the plot
respects equal units on both axes, preserving the layout coordinates.
Works like base R’s asp parameter (y/x aspect ratio)
(#195).pdag_to_dag() function that generates a random DAG
consistent with a given CPDAG/PDAG structure if possible (#201).plot() to use incorrect layout if
node names were not in the same order as in the graph object
(#198).set_edges() so that it correctly replaces
symmetric edges in simple graphs.all in districts() has been
deprecated. Use districts() without arguments to get all
districts.-->), 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.
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