This toolkit implements a numerical solution algorithm 
    to invert a quality of life measure from observed data. Unlike
    the traditional Rosen-Roback measure, this measure accounts for
    mobility frictions—generated by idiosyncratic tastes and local
    ties — and trade frictions — generated by trade costs and
    non-tradable services, thereby reducing non-classical
    measurement error. The QoL measure is based on Ahlfeldt, Bald,
    Roth, Seidel (2024)
    <https://econpapers.repec.org/RePEc:boc:bocode:s459382>
    "Measuring Quality of Life under Spatial Frictions". When using
    this programme or the toolkit in your work, please cite the paper.

Wraps the 'Abseil' 'C++' library for use by R packages.
    Provides both header files and a compiled static library ('libabsl.a') so
    that downstream packages can link non-header-only 'Abseil' components
    without recompiling the library themselves.
    Original files are from <https://github.com/abseil/abseil-cpp>.
    Patches are located at
    <https://github.com/doccstat/abseil-r/tree/main/local/patches>.

An interactive framework for the exploration and analysis of
    adaptive immune receptor repertoire sequencing (AIRR-seq) data. It
    enables large-scale computation and integrated analysis of
    sequence-derived features, including physicochemical properties, amino
    acid descriptor sets, sequence motifs, compositional patterns, and
    somatic hypermutation metrics. The application supports multiscale
    analysis across sequences, clones, and repertoires, with interactive
    visualizations and statistical feature selection. 'AbSolution' also
    facilitates reproducible research by enabling structured export of
    data, code, parameters, and computational environments.  See
    <https://github.com/EDS-Bioinformatics-Laboratory/AbSolution> for more
    details.

A variable selection method using B-Splines in multivariate nOnparametric Regression models Based on partial dErivatives Regularization (ABSORBER) implements a novel variable selection method in a nonlinear multivariate model using B-splines. For further details we refer the reader to the paper Savino, M. E. and Lévy-Leduc, C. (2024), <https://hal.science/hal-04434820>.