Package: KPC
Type: Package
Title: Kernel Partial Correlation Coefficient
Version: 0.1.3
Authors@R: c(person("Zhen", "Huang", email = "zh2395@columbia.edu",
    role = c("aut", "cre")),
    person("Nabarun", "Deb", email = "nd2560@columbia.edu",
    role = "ctb"),
    person("Bodhisattva", "Sen", email = "bodhi@stat.columbia.edu",
    role = "ctb"),
    person("Benjamin", "Lang", email = "benjamin_lang@icloud.com",
    role = "ctb"))
Maintainer: Zhen Huang <zh2395@columbia.edu>
Description: Implementations of two empirical versions the kernel
        partial correlation (KPC) coefficient and the associated
        variable selection algorithms. KPC is a measure of the strength
        of conditional association between Y and Z given X, with X, Y,
        Z being random variables taking values in general topological
        spaces. As the name suggests, KPC is defined in terms of
        kernels on reproducing kernel Hilbert spaces (RKHSs). The
        population KPC is a deterministic number between 0 and 1; it is
        0 if and only if Y is conditionally independent of Z given X,
        and it is 1 if and only if Y is a measurable function of Z and
        X. One empirical KPC estimator is based on geometric graphs,
        such as K-nearest neighbor graphs and minimum spanning trees,
        and is consistent under very weak conditions. The other
        empirical estimator, defined using conditional mean embeddings
        (CMEs) as used in the RKHS literature, is also consistent under
        suitable conditions. Using KPC, a stepwise forward variable
        selection algorithm KFOCI (using the graph based estimator of
        KPC) is provided, as well as a similar stepwise forward
        selection algorithm based on the RKHS based estimator. For more
        details on KPC, its empirical estimators and its application on
        variable selection, see Huang, Z., N. Deb, and B. Sen (2022).
        “Kernel partial correlation coefficient – a measure of
        conditional dependence” (URL listed below). When X is empty,
        KPC measures the unconditional dependence between Y and Z,
        which has been described in Deb, N., P. Ghosal, and B. Sen
        (2020), “Measuring association on topological spaces using
        kernels and geometric graphs” <doi:10.48550/arXiv.2010.01768>,
        and it is implemented in the functions KMAc() and Klin() in
        this package. The latter can be computed in near linear time.
License: GPL-3
Encoding: UTF-8
URL: https://www.jmlr.org/papers/v23/21-493.html,
        https://arxiv.org/abs/2012.14804, https://github.com/zh2395/KPC
LazyData: true
Depends: R (>= 4.0.0), kernlab
Imports: data.table, RANN, proxy, parallel, mlpack
Config/roxygen2/version: 8.0.0
Repository: https://zh2395.r-universe.dev
Date/Publication: 2026-06-19 19:29:19 UTC
RemoteUrl: https://github.com/zh2395/kpc
RemoteRef: HEAD
RemoteSha: 9aa7a7fe04940075a06f603369a7e0b43ce56cc1
NeedsCompilation: no
Packaged: 2026-06-19 20:49:20 UTC; root
Author: Zhen Huang [aut, cre],
  Nabarun Deb [ctb],
  Bodhisattva Sen [ctb],
  Benjamin Lang [ctb]