Filtering, also known as gating, of flow cytometry samples using 
             the curvHDR method, which is described in Naumann, U., Luta, G. and 
             Wand, M.P. (2010) <DOI:10.1186/1471-2105-11-44>.

Automatic specification and estimation of reserve demand curves for central bank operations. The package can help to choose the best demand curve and identify additional explanatory variables. Various plot and predict options are included. For more details, see Chen et al. (2023) <https://www.imf.org/en/Publications/WP/Issues/2023/09/01/Modeling-the-Reserve-Demand-to-Facilitate-Central-Bank-Operations-538754>.

A self-contained native engine (a C interface over 'CUDA'
    kernels and C++ host logic) for stochastic actor-oriented models (the
    model family of 'RSiena'), exponential random graph models
    (cross-sectional, temporal, and separable temporal), and models for
    binary actor attributes, callable from R without a Python runtime.
    Modelled on the 'torch' package: the CRAN build is CPU-only from
    source; the GPU path is compiled from source when a 'CUDA' toolkit is
    detected at configure time. The data preparation, host statistics
    ('RSiena' Appendix B conventions), and moment targets are validated
    bit-for-bit against the reference implementation and reproduce 'RSiena'
    targets on public datasets to machine precision; the estimators match
    'RSiena', 'ergm', 'btergm', and 'tergm' on public benchmark models.

Cobb's maximum likelihood method for cusp-catastrophe modeling
        (Grasman, van der Maas, and Wagenmakers (2009) <doi:10.18637/jss.v032.i08>;
        Cobb (1981), Behavioral Science, 26(1), 75-78). Includes a cusp() function for model 
        fitting, and several utility functions for plotting, and for comparing the
        model to linear regression and logistic curve models.

Use the high-precision arithmetic provided by the R package 'Rmpfr' 
    to compute a custom-made Gauss quadrature nodes and weights, 
    with up to 33 nodes, using a moment-based method via moment 
    determinants. Paul Kabaila (2022) <arXiv:2211.04729>.