Provides Bayesian probabilistic methods for record linkage and entity resolution across multiple datasets using the Comprehensive Hit Or Miss Probabilistic Entity Resolution (CHOMPER) model.
    The package implements three main inference approaches: (1) Evolutionary Variational Inference for record Linkage (EVIL), (2) Coordinate Ascent Variational Inference (CAVI), and (3) Markov Chain Monte Carlo (MCMC) with split and merge process.
    The model supports both discrete and continuous fields, and it performs locally-varying hit mechanism for the attributes with multiple truths.
    It also provides tools for performance evaluation based on either approximated variational factors or posterior samples.
    The package is designed to support parallel computing with multi-threading support for EVIL to estimate the linkage structure faster.

Methods to help selecting General Circulation Models (GCMs) 
    in the context of projecting models to future scenarios. It is provided
    clusterization algorithms, distance and correlation metrics, as well as
    a tailor-made algorithm to detect the optimum subset of GCMs that recreate
    the environment of all GCMs as proposed in Esser et al. (2025) <doi:10.1111/gcb.70008>.

One way to choose the number of principal components is via the reconstruction error. This package is designed mainly for this purpose. Graphical representation is also supported, plus some other principal component analysis related functions. References include: Jolliffe I.T. (2002). Principal Component Analysis. <doi:10.1007/b98835> and Mardia K.V., Kent J.T. and Bibby J.M. (1979). Multivariate Analysis. ISBN: 978-0124712522. London: Academic Press.

Geospatial data computation is parallelized by grid, hierarchy,
    or raster files. Based on 'future' (Bengtsson, 2024 <doi:10.32614/CRAN.package.future>)
    and 'mirai' (Gao et al., 2025 <doi:10.32614/CRAN.package.mirai>) parallel back-ends,
    'terra' (Hijmans et al., 2025 <doi:10.32614/CRAN.package.terra>) and
    'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>)
    functions as well as convenience functions in the package can be distributed
    over multiple threads. The simplest way of parallelizing generic geospatial
    computation is to start from par_pad_*() functions to par_grid(),
    par_hierarchy(), or par_multirasters() functions. Virtually any functions
    accepting classes in 'terra' or 'sf' packages can be used in the three
    parallelization functions. A common raster-vector overlay operation
    is provided as a function extract_at(), which uses
    'exactextractr' (Baston, 2023 <doi:10.32614/CRAN.package.exactextractr>), with
    options for kernel weights for summarizing raster values at vector geometries.
    Other convenience functions for vector-vector operations including
    simple areal interpolation (summarize_aw()) and summation of
    exponentially decaying weights (summarize_sedc()) are also provided.