Package: MaxWiK
Title: Machine Learning Method Based on Isolation Kernel Mean Embedding
Version: 1.0.6
Authors@R: 
    person('Yuri', 'Nagornov', email='nagornov.yuri@gmail.com', role = c('aut', 'cre', 'cph'), comment = c(ORCID = '0000-0002-7935-6776') )
Description: Incorporates Approximate Bayesian Computation to get a
        posterior distribution and to select a model optimal parameter
        for an observation point. Additionally, the meta-sampling
        heuristic algorithm is realized for parameter estimation, which
        requires no model runs and is dimension-independent. A sampling
        scheme is also presented that allows model runs and uses the
        meta-sampling for point generation. A predictor is realized as
        the meta-sampling for the model output. All the algorithms
        leverage a machine learning method utilizing the maxima
        weighted Isolation Kernel approach, or 'MaxWiK'. The method
        involves transforming raw data to a Hilbert space (mapping) and
        measuring the similarity between simulated points and the
        maxima weighted Isolation Kernel mapping corresponding to the
        observation point. Comprehensive details of the methodology can
        be found in the papers Iurii Nagornov (2024)
        <doi:10.1007/978-3-031-66431-1_16> and Iurii Nagornov (2023)
        <doi:10.1007/978-3-031-29168-5_18>.
License: GPL (>= 3)
Depends: R (>= 3.3.0)
Imports: methods, stats, utils, scales, parallel, abc, ggplot2
Suggests: rmarkdown, knitr
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
VignetteBuilder: knitr
LazyData: true
Repository: https://tughall.r-universe.dev
Date/Publication: 2025-07-07 05:11:52 UTC
RemoteUrl: https://github.com/tughall/maxwik
RemoteRef: HEAD
RemoteSha: b54ffce4ca7a3e4f71592fcdf7e6b80d488b7cf7
NeedsCompilation: no
Packaged: 2026-07-02 06:45:27 UTC; root
Author: Yuri Nagornov [aut, cre, cph] (ORCID:
    <https://orcid.org/0000-0002-7935-6776>)
Maintainer: Yuri Nagornov <nagornov.yuri@gmail.com>