Integrating Morphological Modeling and Machine Learning for Decision Support
Create a morphospace of predictor combinations with class probabilitie...
Create a random forest classification model
Turn binary vector into a factor
Turn character vector into a factor
Identity factorization for numbered strings
Heuristic factorization for all columns of a data frame
Heuristic factorization for a single vector
Turn numeric vector into an ordered factor
Zero-padded ordinal labels
Load tabular data (xlsx, csv, or json)
Iterate through app libraries and functions
Launch MLmorph from the source tree (development helper)
MLmorph: Integrating Morphological Modeling and Machine Learning for D...
Launch the MLmorph shiny app
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.