A high level interface for 'torch' providing utilities to reduce the
    the amount of code needed for common tasks, abstract away torch details and 
    make the same code work on both the 'CPU' and 'GPU'. It's flexible enough to
    support expressing a large range of models. It's heavily inspired by 'fastai' by 
    Howard et al. (2020) <doi:10.48550/arXiv.2002.04688>, 'Keras' by Chollet et al. (2015) and 
    'PyTorch Lightning' by Falcon et al. (2019) <doi:10.5281/zenodo.3828935>.

Provides flexible but lightweight logging facilities for R scripts.
    Supports priority levels for logs and messages, flagging messages, capturing
    script output, switching logs, and logging to files or connections.

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) <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>.

Provides stochastic EM algorithms for latent variable models
    with a high-dimensional latent space. So far, we provide functions for confirmatory item
    factor analysis based on the multidimensional two parameter logistic (M2PL) model and the 
    generalized multidimensional partial credit model. These functions scale well for problems
    with many latent traits (e.g., thirty or even more) and are virtually tuning-free.
    The computation is facilitated by multiprocessing 'OpenMP' API.
    For more information, please refer to:
    Zhang, S., Chen, Y., & Liu, Y. (2018). An Improved Stochastic EM Algorithm for Large-scale
    Full-information Item Factor Analysis. British Journal of Mathematical and Statistical
    Psychology. <doi:10.1111/bmsp.12153>.