PsyML: Machine Learning Workflows for Supervised, Unsupervised, and Causal Analysis
Provides single-function pipelines for applied machine learning with tabular data, as commonly found in psychological, behavioral, and health research. The supervised pipeline, supervised_ml(), performs exploratory checks, preprocessing, training of several models with cross-validation, hyperparameter tuning, test-set evaluation, threshold optimization, calibration, fairness analysis, decision curve analysis, and model interpretation with permutation importance and Shapley values. The unsupervised pipeline, unsupervised_ml(), covers k-means, hierarchical, DBSCAN, Gaussian mixture, and latent class clustering with selection of the number of clusters, dimensionality reduction (PCA, t-SNE, UMAP), and anomaly detection (isolation forest, local outlier factor, Mahalanobis distance). The causal pipeline, causal_ml(), estimates treatment effects with propensity scores, meta-learners, double machine learning, causal forests, and targeted maximum likelihood estimation. Also includes learning-curve based sample size estimation, block and diversity-based data splitting, and export of results to text and JSON.
Documentation:
| Reference manual: | PsyML.html , PsyML.pdf |
| Vignettes: |
Sample Size Estimation for Machine Learning (source, R code) Three Approaches: Supervised, Unsupervised, and Causal (source, R code) |
Downloads:
| Package source: | PsyML_2.4.2.tar.gz |
| Windows binaries: | r-devel: not available, r-release: not available, r-oldrel: not available |
| macOS binaries: | r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): not available |
Linking:
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