To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

An interactive 'shiny' training companion for people who are
    new to data, developed for World Health Organization data trainings.
    Bundles hands-on modules for importing data, missing values, outliers,
    text cleaning, merging, visualization, and basic statistics, plus a set
    of AI-skills pages (prompting levels, a prompt gallery, AI safety rules,
    and a methodology case study) and an interactive quiz with per-session
    topic filters. Launch the app with run_cata().

Access public spatial data from the Spanish 'Catastro'
    through its 'INSPIRE' and related web services. Retrieve parcel,
    building, address and map image data, and convert between parcel
    references and coordinates.

Performs classification and variable selection on high-dimensional tensors (multi-dimensional arrays) after adjusting for additional covariates (scalar or vectors) as CATCH model in Pan, Mai and Zhang (2018) <arXiv:1805.04421>. The low-dimensional covariates and the high-dimensional tensors are jointly modeled to predict a categorical outcome in a multi-class discriminant analysis setting. The Covariate-Adjusted Tensor Classification in High-dimensions (CATCH) model is fitted in two steps: (1) adjust for the covariates within each class; and (2) penalized estimation with the adjusted tensor using a cyclic block coordinate descent algorithm. The package can provide a solution path for tuning parameter in the penalized estimation step. Special case of the CATCH model includes linear discriminant analysis model and matrix (or tensor) discriminant analysis without covariates.