Probabilistic time series forecasting via Natural Gradient Boosting for Probabilistic Prediction.

Posterior distribution of case-control fine-mapping. Specifically, Bayesian variable selection for single-nucleotide polymorphism (SNP) data using the normal-gamma prior. Alenazi A.A., Cox A., Juarez M,. Lin W-Y. and Walters, K. (2019) Bayesian variable selection using partially observed categorical prior information in fine-mapping association studies, Genetic Epidemiology. <doi:10.1002/gepi.22213>.

Next-Generation Clustered Heat Maps (NG-CHMs) allow for
    dynamic exploration of heat map data in a web browser. 'NGCHM' allows
    users to create both stand-alone HTML files containing a
    Next-Generation Clustered Heat Map, and .ngchm files to view in the
    NG-CHM viewer. See Ryan MC, Stucky M, et al (2020)
    <doi:10.12688/f1000research.20590.2> for more details.

Provides an 'htmlwidgets' <https://www.htmlwidgets.org/> interface to 'NGL.js' <http://nglviewer.org/ngl/api/>.
    'NGLvieweR' can be used to visualize and interact with protein databank ('PDB') and structural files in R and Shiny applications.
    It includes a set of API functions to manipulate the viewer after creation in Shiny.

Fits and analyzes linear latent non-Gaussian models for
    temporal, spatial, and space-time data. The package provides model
    components for autoregressive and Ornstein-Uhlenbeck processes, random
    walks, Matern fields based on stochastic partial differential equations,
    separable and non-separable space-time models, graph-based Matern models,
    bivariate type-G fields, and user-defined sparse operators. Latent fields
    and observation models can use Gaussian and non-Gaussian noise
    distributions, including normal inverse Gaussian, generalized asymmetric
    Laplace, and skew-t distributions. Functions are included for simulation,
    likelihood-based estimation, prediction, cross-validation, convergence
    diagnostics, stochastic gradient optimization, batch-means confidence
    intervals, and posterior-like sampling. The modeling framework is described
    in Bolin, Jin, Simas and Wallin (2026) "A Unified and Computationally
    Efficient Non-Gaussian Statistical Modeling Framework"
    <doi:10.48550/arXiv.2602.23987>.