Integrating Morphological Modeling and Machine Learning for Decision Support
Overview
Integrating morphological modeling with machine learning to support structured decision-making (e.g., in management and consulting). The package enumerates a morphospace of feasible configurations and uses random forests to estimate class probabilities over that space, bridging deductive model exploration with empirical validation. It includes utilities for factorizing inputs, model training, morphospace construction, and an interactive 'shiny' app for scenario exploration.
- Fixing imports.
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
- OK13 OK2026-06-09
Show 4 earlier changesHide 4 earlier changes
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-05-02
- ERROR11 OK, 1 ERROR2026-04-25
- OK14 OK2026-03-10
Documentation
- Help pages
- 14 (4 internal)includes a package overview page
- Examples that run
- 100%10 run in a plain check (1 of them under a condition)
- Exported functions with examples
- 91%
- Documented parameters
- 98%
- Return-value docs
- 91%
- Help pages citing references
- 0%
Downloads
Repository
Releases over time
4 development-tooling and community-health practices detected across 2 families in the upstream repository
Checks run against github.com/theogrost/mlmorph on 2026-10-18.
Dependencies
Installing MLmorph adds 107 packages (57 with compiled code, 85 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
People & History
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- RR 4.6.0 released · 2026-04-24
- 0.1.1Latest
- 0.1.02025-09-02
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2025-09-02
- Total releases
- 2 / 1 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 4.3.0
- Download size
- 34 KB
Cite
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Run in R for the authors' preferred citation:
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