Offers a streamlined programmatic interface to Ordnance Survey's 
    British National Grid (BNG) index system, enabling efficient spatial 
    indexing and analysis based on grid references. It supports a range of 
    geospatial applications, including statistical aggregation, data 
    visualisation, and interoperability across datasets. Designed for 
    developers and analysts working with geospatial data in Great Britain, 
    'osbng' simplifies integration with geospatial workflows and provides 
    intuitive tools for exploring the structure and logic of the BNG system.

Optimal Subset Cardinality Regression (OSCAR) models offer
    regularized linear regression using the L0-pseudonorm, conventionally
    known as the number of non-zero coefficients. The package estimates an
    optimal subset of features using the L0-penalization via
    cross-validation, bootstrapping and visual diagnostics. Effective
    Fortran implementations are offered along the package for finding
    optima for the DC-decomposition, which is used for transforming the
    discrete L0-regularized optimization problem into a continuous
    non-convex optimization task. These optimization modules include DBDC
    ('Double Bundle method for nonsmooth DC optimization' as described in
    Joki et al. (2018) <doi:10.1137/16M1115733>) and LMBM ('Limited
    Memory Bundle Method for large-scale nonsmooth optimization' as
    in Haarala et al. (2004) <doi:10.1080/10556780410001689225>). The
    OSCAR models are comprehensively exemplified in Halkola et al. (2023) 
    <doi:10.1371/journal.pcbi.1010333>). Multiple regression model families
    are supported: Cox, logistic, and Gaussian.

A collection of general optimization routines based on variants of 
    the One Side Cut Accelerated Random Search (OSCARS-II) algorithm 
    (Price et al., 2020, <doi:10.1007/s10898-020-00928-6>). The main function
    , 'oscars()', performs black-box optimization of a general (including nonsmooth or 
    discontinuous) function subject to simple bounds on the unknowns. If all bounds are 
    finite, oscars searches globally. The main method implements a stochastic direct 
    search method and is derivative free.   Testing shows the OSCARS-II algorithm 
    usually finds extrema with fewer function evaluations than similar global 
    derivative-free methods.