MaxWiK
1.0.6Machine Learning Method Based on Isolation Kernel Mean Embedding
Overview
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
- OK2026-08-0513 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- NOTE2026-08-0112 OK · 1 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
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Code & Tests
Datasets
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 1.0.6Latest
- RR 4.5.0 released · 2025-04-11
- 1.0.52024-11-25
- RR 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
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Run in R for the authors' preferred citation:
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