Implementation of statistical methods for the estimation of
    toroidal diffusions. Several diffusive models are provided, most of them
    belonging to the Langevin family of diffusions on the torus. Specifically,
    the wrapped normal and von Mises processes are included, which can be seen
    as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
    methods for approximate maximum likelihood estimation, organized in four
    blocks, is given: (i) based on the exact transition probability density,
    obtained as the numerical solution to the Fokker-Plank equation; (ii) based
    on wrapped pseudo-likelihoods; (iii) based on specific analytic
    approximations by wrapped processes; (iv) based on maximum likelihood of
    the stationary densities. The package allows the replicability of the
    results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.

Identify 17 Sustainable Development Goals and associated 169 targets in text.

Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025).

Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) <doi:10.1111/2041-210X.13506>.