Package: PsyML
Type: Package
Title: Machine Learning Workflows for Supervised, Unsupervised, and
        Causal Analysis
Version: 2.4.2.9000
Date: 2026-10-25
Authors@R: c(
    person("Jose", "Ventura-Leon",
           email = "jventuraleon@gmail.com",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2996-4244"))
    )
Description: 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.
License: MIT + file LICENSE
URL: https://github.com/jventural/PsyML
BugReports: https://github.com/jventural/PsyML/issues
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.3.2
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
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/pak/sysreqs: cmake make libicu-dev libuv1-dev
Repository: https://jventural.r-universe.dev
Date/Publication: 2026-10-29 07:45:50 UTC
RemoteUrl: https://github.com/jventural/psyml
RemoteRef: HEAD
RemoteSha: 992d6b7852c38e65ae43abfb0364557778258bcc
NeedsCompilation: no
Packaged: 2026-11-13 13:34:45 UTC; root
Author: Jose Ventura-Leon [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2996-4244>)
Maintainer: Jose Ventura-Leon <jventuraleon@gmail.com>