Package: cdzoo 0.2.0.9000

cdzoo: A Multi-Backend Benchmark Harness and Recommender for Causal Discovery

Provides a single typed interface over a curated portfolio of causal-structure-discovery algorithms drawn from several R and Python back-ends, together with a structural-causal-model scenario grid, fair graph-recovery metrics, and an evidence-routed recommender that inspects a data set and suggests a discovery method suited to its characteristics. Every back-end is optional and loaded lazily only when used, so the harness, the scenario grid, the metrics, and the recommender's routing logic all run with no suggested package installed.

Authors:Max Moldovan [aut, cre, cph]

cdzoo_0.2.0.9000.tar.gz
cdzoo_0.2.0.9000.zip(r-4.7-any)cdzoo_0.2.0.9000.zip(r-4.6-any)cdzoo_0.2.0.9000.zip(r-4.5-any)
cdzoo_0.2.0.9000.tgz(r-4.6-any)cdzoo_0.2.0.9000.tgz(r-4.5-any)
cdzoo_0.2.0.9000.tar.gz(r-4.7-any)cdzoo_0.2.0.9000.tar.gz(r-4.6-any)
cdzoo_0.2.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
cdzoo/json (API)

# Install 'cdzoo' in R:
install.packages('cdzoo', repos = c('https://max578.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/max578/cdzoo/issues

Datasets:

On CRAN:

Conda:

3.90 score 19 exports 2 dependencies

Last updated from:673a39a81c (on main). Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK194
source / vignettesOK210
linux-releaseOK174
macos-releaseOK141
macos-oldrelOK121
windows-develOK85
windows-releaseOK98
windows-oldrelOK86
wasm-releaseOK113

Exports:as_orchestra_manifestcd_autocd_benchmarkcd_correlation_from_countscd_diagnosecd_discovercd_discover_suffstatcd_edgescd_explaincd_methodcd_resultcd_scenariocd_scenarioscd_scorecd_suffstatlist_methodsorchestra_manifestregister_methodverify_manifest

Dependencies:digestS7

A benchmark harness and recommender for causal discovery
Why | What | Do | The registry | Running one method directly | The recommender explains itself | When unsure, ask several: consensus | The benchmark evidence behind the routing | Scoring and benchmarking your own runs | Beyond a sample: an evaluable density | Read | Limits | What to read next | Reproduce
Rendered fromcdzoo.Rmdusingknitr::rmarkdown

Last update: 2026-09-02
Started: 2026-06-24

Discovery from sufficient statistics
Why | What | Do | From counts to a correlation matrix | Discovery directly from the statistic | Two ways to prune, at a sample size large enough to matter | Letting the recommender route it | Passing the result on | Read | Limits | What to read next | Reproduce
Rendered fromsuffstat.Rmdusingknitr::rmarkdown

Last update: 2026-09-02
Started: 2026-07-05