Package: OrdCD
Type: Package
Title: Ordinal Causal Discovery
Version: 1.1.2
Date: 2023-05-14
Authors@R: person(given = "Yang", family = "Ni", email = "yni@stat.tamu.edu", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-0636-2363"))
Description: Algorithms for ordinal causal discovery. This package aims
        to enable users to discover causality for observational ordinal
        categorical data with greedy and exhaustive search. See Ni, Y.,
        & Mallick, B. (2022)
        <https://proceedings.mlr.press/v180/ni22a/ni22a.pdf> "Ordinal
        Causal Discovery. Proceedings of the 38th Conference on
        Uncertainty in Artificial Intelligence, (UAI 2022), PMLR
        180:1530–1540".
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
Imports: gRbase, MASS, bnlearn, igraph, stats, Matrix
URL: https://github.com/nySTAT/OrdCD
BugReports: https://github.com/nySTAT/OrdCD/issues
Config/pak/sysreqs: libglpk-dev libxml2-dev
Repository: https://nystat.r-universe.dev
Date/Publication: 2023-05-14 20:14:21 UTC
RemoteUrl: https://github.com/nystat/ordcd
RemoteRef: HEAD
RemoteSha: 8b6080987119d2412e1e9ff12bd92b19f982eeae
NeedsCompilation: no
Packaged: 2026-10-11 12:33:11 UTC; root
Author: Yang Ni [aut, cre] (ORCID: <https://orcid.org/0000-0003-0636-2363>)
Maintainer: Yang Ni <yni@stat.tamu.edu>