Machine Learning Workflows for Supervised, Unsupervised, and Causal Analysis R package PsyML version 2.4.2 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. Software 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 Jose Ventura-Leon <jventuraleon@gmail.com> Comprehensive R Archive Network (CRAN) Jose Ventura-Leon [aut, cre] (ORCID: <https://orcid.org/0000-0003-2996-4244>) MIT + file LICENSE (https://CRAN.R-project.org/package=PsyML/LICENSE) 2026-11-12 application/tgz https://CRAN.R-project.org/package=PsyML doi:10.32614/CRAN.package.PsyML