Implementation of a statistical approach 
  for estimating the joint health effects of multiple 
  concurrent exposures, as described in Bobb et al (2015) 
  <doi:10.1093/biostatistics/kxu058>.

Bayesian kernel machine regression (from the 'bkmr' package)
    is a Bayesian semi-parametric generalized linear model approach under
    identity and probit links. There are a number of functions in this
    package that extend Bayesian kernel machine regression fits to allow
    multiple-chain inference and diagnostics, which leverage functions
    from the 'future', 'rstan', and 'coda' packages.  Reference: Bobb, J.
    F., Henn, B. C., Valeri, L., & Coull, B. A. (2018). Statistical
    software for analyzing the health effects of multiple concurrent
    exposures via Bayesian kernel machine regression. ;
    <doi:10.1186/s12940-018-0413-y>.

Implements the Beta Kernel Process (BKP) for nonparametric modeling of spatially varying binomial probabilities, together with its extension, the Dirichlet Kernel Process (DKP), for categorical or multinomial data.
 The package provides functions for model fitting, predictive inference with uncertainty quantification, posterior simulation, and visualization in one-and two-dimensional input spaces.
 Multiple kernel functions (Gaussian, Matern 5/2, and Matern 3/2) are supported, with hyperparameters optimized through multi-start gradient-based search.
 For more details, see Zhao, Qing, and Xu (2025) <doi:10.48550/arXiv.2508.10447>.

Fitting, cross-validating, and predicting with Bayesian Knowledge Tracing (BKT) models. It is designed for analyzing educational datasets to trace student knowledge over time. The package includes functions for fitting BKT models, evaluating their performance using various metrics, and making predictions on new data. It provides the similar functionality as the Python package pyBKT authored by Zachary A. Pardos (zp@berkeley.edu) at <https://github.com/CAHLR/pyBKT>.

Facilitates scalable spatiotemporally varying coefficient
    modelling with Bayesian kernelized tensor regression.
    The important features of this package are:
    (a) Enabling local temporal and spatial modeling of the relationship between
    the response variable and covariates.
    (b) Implementing the model described by Lei et al. (2023) <doi:10.48550/arXiv.2109.00046>.
    (c) Using a Bayesian Markov Chain Monte Carlo (MCMC) algorithm to sample from the posterior
    distribution of the model parameters.
    (d) Employing a tensor decomposition to reduce the number of estimated parameters.
    (e) Accelerating tensor operations and enabling graphics processing unit (GPU) acceleration
    with the 'torch' package.