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MaxWiK

1.0.6

Machine Learning Method Based on Isolation Kernel Mean Embedding

0packages depend
2Kdownloads / year
test coverage
13/13checks pass

Overview

About
Maintained by Yuri NagornovFirst published 2024-11-252 releasesCRAN page ↗

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.

Install

Health

CRAN checks
13OK
Slowest check: 3.3 min · r-oldrel-windows-x86_64
Code health
None
Tests · ratio 0.00
not tracked
Coverage
100%
Documentation · exports
7
Dependencies · direct
Check history
  • OK2026-08-05
    13 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • NOTE2026-08-01
    12 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
  • OK2026-03-10
    14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE

Documentation

Documentation
READMEYes · 542 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
0%

Downloads

2K
CRAN downloads in the past year
Rank #23,098 · ~5/day · ~163/mo
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10630 days
44490 days
2K1 year
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Dependencies

Declared dependencies
5 external dependencies (excludes base and recommended)
Depends (1)
R >= 3.3.0
Imports (7)
methodsstatsutilsscalesparallelabcggplot2
LinkingTo (0)
none
Suggests (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

Code & Tests

Datasets

People & History

People (1)
Maintainer (1)
Author, Maintainer, Copyright holder
Authors (1)
Author, Maintainer, Copyright holder
Copyright holders (1)
Author, Maintainer, Copyright holder
Package Timeline

2 releases. Pick two to compare their code metrics. R releases are shown for context.

  • R
    R 4.6.0 released · 2026-04-24
  • 1.0.6Latest
    2025-07-07 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 1.0.5
    2024-11-25
  • R
    R 4.4.0 released · 2024-04-24

Package metadata

First published
2024-11-25
Total releases
2 / 2 yrs
License
GPL (>= 3) OSI
Minimum R
≥ 3.3.0
Bundled data
131 KB / 1 file
Download size
561 KB
Installed size
not tracked yet
With dependencies
not tracked yet

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("MaxWiK")
Nagornov, Y. (2025). MaxWiK: Machine Learning Method Based on Isolation Kernel Mean Embedding (Version 1.0.6) [Computer software]. https://doi.org/10.32614/CRAN.package.MaxWiK

This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for MaxWiK version 1.0.6 [Data set]. HJJB, LLC. Data release v2026-08-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-08-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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