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cff-version: 1.2.0
message: 'To cite package "MaxWiK" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'MaxWiK: Machine Learning Method Based on Isolation Kernel Mean Embedding'
version: 1.0.5
doi: 10.32614/CRAN.package.MaxWiK
abstract: 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) <https://doi.org/10.1007/978-3-031-66431-1_16>
  and Iurii Nagornov (2023) <https://doi.org/10.1007/978-3-031-29168-5_18>.
authors:
- family-names: Nagornov
  given-names: Yuri
  email: nagornov.yuri@gmail.com
  orcid: https://orcid.org/0000-0002-7935-6776
repository: https://tughall.r-universe.dev
commit: d0bf433ebad3c22b6609bc0d67ba9bb5e076d3c9
contact:
- family-names: Nagornov
  given-names: Yuri
  email: nagornov.yuri@gmail.com
  orcid: https://orcid.org/0000-0002-7935-6776