Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

Includes five particle filtering algorithms for use with state space
    models in the 'nimble' system: 'Auxiliary', 'Bootstrap', 'Ensemble Kalman filter',
    'Iterated Filtering 2', and 'Liu-West', as described in Michaud et al. (2021),
    <doi:10.18637/jss.v100.i03>. A full User Manual is available at
    <https://r-nimble.org>.

A software package to perform Wombling, or boundary analysis, using the 'nimble' Bayesian hierarchical modeling environment. Wombling is used widely to track regions of rapid change within the spatial reference domain. Specific functions in the package implement Gaussian process models for point-referenced spatial data followed by predictive inference on rates of change over curves using line integrals. We demonstrate model based Bayesian inference using posterior distributions featuring simple analytic forms while offering uncertainty quantification over curves. For more details on wombling please see, Banerjee and Gelfand (2006) <doi:10.1198/016214506000000041> and Halder, Banerjee and Dey (2024) <doi:10.1080/01621459.2023.2177166>.

Principal Components Analysis of a matrix using Non-linear
    Iterative Partial Least Squares or weighted Expectation Maximization
    PCA with Gram-Schmidt orthogonalization of the scores and loadings.
    Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.