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.

Version:	2.4.2
Depends:	R (≥ 4.1.0)
Imports:	dials, dplyr, ggplot2, grDevices, graphics, hardhat, jsonlite, nnet, parallel, parsnip, pROC, ranger, recipes, rlang, rsample, stats, tibble, tidyr, tidyselect, tune, utils, workflows, xgboost, yardstick
Suggests:	Boruta, bslib, cluster, cobalt, commonmark, dbscan, doParallel, DoubleML, DT, e1071, finetune, flextable, foreach, ggdendro, glmnet, grf, htmltools, httr, httr2, isotree, kernelshap, kernlab, knitr, MatchIt, mclust, mlr3, mlr3learners, officer, patchwork, poLCA, readxl, reticulate, rmarkdown, rpart, Rtsne, shapviz, shiny, shinydashboard, SuperLearner, testthat (≥ 3.0.0), themis, tmle, umap, VIM
Published:	2026-11-12
DOI:	10.32614/CRAN.package.PsyML (may not be active yet)
Author:	Jose Ventura-Leon [aut, cre]
Maintainer:	Jose Ventura-Leon <jventuraleon at gmail.com>
BugReports:	https://github.com/jventural/PsyML/issues
License:	MIT + file LICENSE
URL:	https://github.com/jventural/PsyML
NeedsCompilation:	no
Materials:	README, NEWS
CRAN checks:	PsyML results

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): PsyML_2.4.2.tgz, r-release (x86_64): not available, r-oldrel (x86_64): not available

Linking:

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