Creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Provides tools for the integration, visualisation, and modelling of spatial epidemiological data using the method described in Azeez, A., & Noel, C. (2025). 'Predictive Modelling and Spatial Distribution of Pancreatic Cancer in Africa Using Machine Learning-Based Spatial Model' <doi:10.5281/zenodo.16529986> and <doi:10.5281/zenodo.16529016>. It facilitates the analysis of geographic health data by combining modern spatial mapping tools with advanced machine learning (ML) algorithms. 'mlspatial' enables users to import and pre-process shapefile and associated demographic or disease incidence data, generate richly annotated thematic maps, and apply predictive models, including Random Forest, 'XGBoost', and Support Vector Regression, to identify spatial patterns and risk factors. It is suited for spatial epidemiologists, public health researchers, and GIS analysts aiming to uncover hidden geographic patterns in health-related outcomes and inform evidence-based interventions.

Provides tools for multilevel descriptive statistics and data preparation.
    Computes within-group and between-group correlations (via variance decomposition or
    two-level structural equation modeling), intraclass correlation coefficients (ICCs),
    and descriptive statistics for nested data (e.g., repeated measurements per person),
    supporting both frequentist (via 'lme4' or 'lavaan') and Bayesian (via 'brms')
    estimation. Results are formatted according to APA standards and can be exported as
    tables using 'gt' or 'tinytable'. Also includes functions for decomposing variables
    into within-group and between-group components for use in Random Effects
    Within-Between (REWB) models.

Fits latent Dirichlet allocation (LDA), supervised topic models,
    and multilevel supervised topic models for text data with multiple
    outcome variables. Core estimation routines are implemented in C++
    using the 'Rcpp' ecosystem. 
    For topic models, see Blei et al. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>.
    For supervised topic models, see Blei and McAuliffe (2007) <https://papers.nips.cc/paper_files/paper/2007/hash/d56b9fc4b0f1be8871f5e1c40c0067e7-Abstract.html>.