Fit response surfaces for datasets with latent-variable Gaussian
    process modeling, predict responses for new inputs, and plot latent
    variables locations in the latent space (only 1D or 2D). The input
    variables of the datasets can be quantitative, qualitative/categorical or
    mixed. The output variable of the datasets is a scalar (quantitative). The
    optimization of the likelihood function is done using a successive
    approximation/relaxation algorithm similar to another GP modeling package
    "GPM". The modeling method is published in "A Latent Variable Approach to
    Gaussian Process Modeling with Qualitative and Quantitative Factors" by
    Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018)
    <doi:10.48550/arXiv.1806.07504>. The package is developed in IDEAL of
    Northwestern University.

Calculate point estimates of and valid confidence intervals for
    longitudinal summaries of nonparametric, algorithm-agnostic variable importance measures. 
    For more details, see Williamson et al. (2024) <doi:10.48550/arXiv.2311.01638>.

Contains a collection of useful
    functions for basic data computation and manipulation,
    wrapper functions for generating 'ggplot2' graphics,
    including statistical model diagnostic plots, methods
    for computing statistical models quality measures (such
    as AIC, BIC, r squared, root mean squared error) and
    general utilities.

Converts output from latent variable model tools into
    publication-ready path diagrams and model schematics. 'lavaan' fit objects
    and parameter tables are supported as a primary workflow, with graph
    adapters for objects from 'blavaan', 'lavaan.mi', 'semPlot', 'mirt',
    'eRm', 'OpenMx', 'psych', 'poLCA', 'mclust', 'flexmix', 'lcmm',
    'tidyLPA', and 'MplusAutomation' workflows when those packages are
    available. Supports structural equation and confirmatory factor analysis
    diagrams, multilevel structural equation models, growth models,
    higher-order factor models, latent class and profile models, item response
    theory models, and common mixture outputs through a unified graph grammar
    with model-aware defaults, geometry diagnostics, layout quality scoring,
    automatic layout selection, customizable publication styles, 'RStudio'
    preview, SVG/PDF/PNG export, 'TikZ' output, and reproducible publication
    bundles.