Machine 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.
**2025 July 7th**
Update and fix of theme usage for ggplot2 package.
From NEWS.md in the release
Install
Health
- OK13 OK2026-09-08
- ERROR12 OK, 1 ERROR2026-09-07
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK14 OK2026-03-10
Documentation
- Help pages
- 23 (14 internal)includes a package overview page
- Examples that run
- 100%21 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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- RR 4.6.0 released · 2026-04-24
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- 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
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