Maximum likelihood estimation for stochastic frontier
    analysis (SFA) of production (profit) and cost functions. The package
    includes the basic stochastic frontier for cross-sectional or pooled
    data with several distributions for the one-sided error term (i.e.,
    Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential
    and truncated skewed Laplace), the latent class stochastic frontier
    model (LCM) as described in Dakpo et al. (2021)
    <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data,
    and the sample selection model as described in Greene (2010)
    <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021)
    <doi:10.1111/agec.12683>.  Several possibilities in terms of
    optimization algorithms are proposed.

Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.

Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".

Compute centrographic statistics (central points, standard 
    distance, standard deviation ellipse, standard deviation box) for 
    observations taken at point locations in 2D or 3D. The 'sfcentral' library 
    was inspired in 'aspace' package but conceived to be used in a spatial
    'tidyverse' context.

Bayesian clustering of spatial regions with similar functional shapes using
    spanning trees and latent Gaussian models. The method enforces spatial contiguity
    within clusters and supports a wide range of latent Gaussian models, including
    non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong,
    R., Chacón-Montalván, E. A., and Moraga, P. (2024) <doi:10.48550/arXiv.2407.12633>,
    extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023)
    <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence
    diagnostics, visualization, and summarization of clustering results.