Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.

Miscellaneous functions for analysing species association
        and niche overlap.

Implements the Stratigraphic Plug Alignment (SPA) procedure for
    integrating sparsely sampled plug-based measurements (e.g., total organic
    carbon, porosity, mineralogy) with high-resolution X-ray fluorescence
    (XRF) geochemical data. SPA uses linear interpolation via the base
    approx() function with constrained extrapolation (rule = 1) to preserve
    stratigraphic order and avoid estimation beyond observed depths. The
    method aligns all datasets to a common depth grid, enabling high-resolution
    multivariate analysis and stratigraphic interpretation of core-based
    datasets such as those from the Utica and Point Pleasant formations.
    See R Core Team (2025)
    <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html>
    and Omodolor (2025)
    <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524>
    for methodological background and geological context.

Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.