Detect outlying observations in functional data sets based on the minimum regularized covariance trace (MRCT) estimator. Includes implementation of Oguamalam et al. (2023) <arXiv:2307.13509>.

Provides functions for analyzing the association between
    one single response categorical variable (SRCV) and one multiple response
    categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
    chi-square statistic can be used to test for marginal independence for the one or
    two MRCV case, or a more general loglinear modeling approach can be used to examine
    various other structures of association for the two or three MRCV case.  Bootstrap-
    and asymptotic-based standardized residuals and model-predicted odds ratios are
    available, in addition to other descriptive information. Statisical methods implemented 
    are described in Bilder et al. (2000) <doi:10.1080/03610910008813665>,
    Bilder and Loughin (2004) <doi:10.1111/j.0006-341X.2004.00147.x>,
    Bilder and Loughin (2007) <doi:10.1080/03610920600974419>,
    and Koziol and Bilder (2014) <https://journal.r-project.org/articles/RJ-2014-014/>.

Animal abundance estimation via conventional, multiple covariate
    and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
    fitting is performed via maximum likelihood. Also included are diagnostics
    and plotting for fitted detection functions. Abundance estimation is via a
    Horvitz-Thompson-like estimator.

Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.

Explore and retrieve marine spatial data from the Marine Regions Gazetteer <https://marineregions.org/gazetteer.php?p=webservices> and the Marine Regions Data Products <https://marineregions.org/webservices.php>.

Model fitting, sampling and visualization
    for the (Hidden) Markov Random Field model with pairwise interactions and 
    general interaction structure from 
    Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>,
    which has many popular models used in 2-dimensional lattices
    as particular cases, like the Ising Model and Potts Model.
    A complete manuscript describing the package is available in
    Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.